efficient communication protocols for underwater

EFFICIENT COMMUNICATION PROTOCOLS FORUNDERWATER ACOUSTIC SENSOR NETWORKS

A ThesisPresented to

The Academic Faculty

by

Dario Pompili

In Partial Fulfillmentof the Requirements for the Degree

Doctor of Philosophy in theSchool of Electrical and Computer Engineering

Georgia Institute of TechnologyAugust 2007

EFFICIENT COMMUNICATION PROTOCOLS FORUNDERWATER ACOUSTIC SENSOR NETWORKS

Approved by:

Professor Ian F. Akyildiz, AdvisorSchool of Electrical and ComputerEngineeringGeorgia Institute of Technology

Professor William D. HuntSchool of Electrical and ComputerEngineeringGeorgia Institute of Technology

Professor Faramarz FekriSchool of Electrical and ComputerEngineeringGeorgia Institute of Technology

Professor Mostafa H. AmmarCollege of ComputingGeorgia Institute of Technology

Professor Raghupathy SivakumarSchool of Electrical and ComputerEngineeringGeorgia Institute of Technology

Date Approved: June 5th, 2007

Ad Alessandra

iii

ACKNOWLEDGEMENTS

The author wishes to thank most sincerely Prof. Ian F. Akyildiz for his continuing guid-

ance in the completion of this work, as well as for his valuable support as advisor during

the entire Ph.D. program. His mentorship was paramount in providing a well rounded ex-

perience, which I will treasure in my career.

To all the academic members of the Electrical and Computer Engineering Department

at the Georgia Institute of Technology, I wish to express my deepest gratitude for excellent

advice, constructive criticism, helpful and critical reviews throughout the Ph.D. program.

A special thank goes to Drs. Fekri, Sivakumar, Hunt, and Ammar, who kindly agreed

to serve in my Ph.D. Defense Committee.

The author is indebt to his friend and colleague Tommaso Melodia for all the valuable

work done together during the completion of the Ph.D. program. As well, the author would

like to thank all former and current members of the Broadband and Wireless Networking

Laboratory for sharing this learning experience.

Last but not least, the author is grateful to the many anonymous reviewers that with

their unselfish comments greatly improved the content of the papers from which this thesis

has been partly extracted.

iv

TABLE OF CONTENTS

DEDICATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv

LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii

LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii

I INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . 5

II RESEARCH CHALLENGES FOR UNDERWATER ACOUSTIC SENSOR NET-WORKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.2 Communication Architectures . . . . . . . . . . . . . . . . . . . . . . . 8

2.3 Design Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.4 Basics of Underwater Acoustic Propagation . . . . . . . . . . . . . . . . 21

2.5 Physical Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.6 Data Link Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.7 Network Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

2.8 Transport Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

2.9 Application Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

2.10 Experimental Implementations of Underwater Sensor Networks . . . . . 40

III DEPLOYMENT ANALYSIS FOR UNDERWATER ACOUSTIC SENSOR NET-WORKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

3.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

3.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

3.3 Communication Architectures . . . . . . . . . . . . . . . . . . . . . . . 43

3.4 Deployment in a 2D Environment . . . . . . . . . . . . . . . . . . . . . 45

v

3.5 Deployment in a 3D Environment . . . . . . . . . . . . . . . . . . . . . 62

IV DISTRIBUTED ROUTING ALGORITHMS FOR UNDERWATER ACOUSTICSENSOR NETWORKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

4.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

4.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

4.3 Network Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

4.4 Packet Train and Optimal Packet Size . . . . . . . . . . . . . . . . . . . 73

4.5 Delay-insensitive Routing Algorithm . . . . . . . . . . . . . . . . . . . 85

4.6 Delay-sensitive Routing Algorithm . . . . . . . . . . . . . . . . . . . . 89

4.7 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 94

V A RESILIENT ROUTING ALGORITHM FOR LONG-TERM UNDERWATERMONITORING MISSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

5.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

5.2 Basics of the Resilient Routing Algorithm . . . . . . . . . . . . . . . . . 108


VI A CDMA MEDIUM ACCESS CONTROL PROTOCOL FOR UNDERWATERACOUSTIC SENSOR NETWORKS . . . . . . . . . . . . . . . . . . . . . . 122

6.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

6.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

6.3 UW-MAC: A Distributed CDMA MAC for UW-ASNs . . . . . . . . . . 126

6.4 Power and Code Self-assignment Problem . . . . . . . . . . . . . . . . . 130


VII CROSS-LAYER COMMUNICATION FOR MULTIMEDIA APPLICATIONSIN UNDERWATER ACOUSTIC SENSOR NETWORKS . . . . . . . . . . . 150

7.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

7.2 Cross-layer Resource Allocation Framework . . . . . . . . . . . . . . . 153

7.3 Cross-layer Routing/MAC/PHY Solution for Delay-tolerant Applications 161

7.4 Cross-layer Routing/MAC/PHY Solution for Delay-sensitive Applications168

7.5 Protocol Operation of the Cross-layer Solution . . . . . . . . . . . . . . 171

vi

VIII CONCLUSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174

LIST OF ACRONYMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188

VITA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191

vii

LIST OF TABLES

1 Available bandwidth for different ranges in UW-A channels . . . . . . . . . 21

2 Evolution of modulation technique . . . . . . . . . . . . . . . . . . . . . . 26

3 Redundant sensors∆N∗ to compensate for failures . . . . . . . . . . . . . 62

4 Simulation performance parameters . . . . . . . . . . . . . . . . . . . . . 94

5 Scenarios 2 and 3: Surface Station and Average Energy per Bit . . . . . . . 101

6 Source Block Probability (SBP) vs. Observation Time . . . . . . . . . . . . 114

viii

LIST OF FIGURES

1 Architecture for 2D underwater sensor networks . . . . . . . . . . . . . . . 10

2 Architecture for 3D underwater sensor networks . . . . . . . . . . . . . . . 12

3 Architecture for 3D underwater sensor networks with AUVs . . . . . . . . 13

4 Internal organization of an underwater sensor node . . . . . . . . . . . . . 17

5 Path loss of short range shallow UW-A channels vs. distance and frequencyin band1− 50 kHz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

6 Triangular-grid deployment.Grid structure and side margins . . . . . . . . 47

7 Triangular-grid deployment.Uncovered area . . . . . . . . . . . . . . . . 47

8 Triangular-grid deployment.Sensing coverage . . . . . . . . . . . . . . . 49

9 Minimum number of sensors in triangular-grid deployment vs. sensor dis-tance over sensing range.A1 = 100x100 m2 . . . . . . . . . . . . . . . . . 51

10 Minimum number of sensors in triangular-grid deployment vs. sensor dis-tance over sensing range.A2 = 300x200 m2 . . . . . . . . . . . . . . . . . 51

11 Minimum number of sensors in triangular-grid deployment vs. sensor dis-tance over sensing range.A3 = 1000x1000 m2 . . . . . . . . . . . . . . . 52

12 Trajectory of a sinking object . . . . . . . . . . . . . . . . . . . . . . . . . 53

13 Average horizontal displacement of sensors and uw-gateways vs. currentvelocity (for three different depths) . . . . . . . . . . . . . . . . . . . . . . 58

14 Maximum and average sensor-gateway distance vs. number of deployedgateways (in three different volumes, and withvmax

c = 1 m/s) . . . . . . . 59

15 Normalized average and standard deviation of number of sensors per uw-gateway vs. number of deployed gateways (for grid and random deploy-ment strategies, in three different volumes, and withvmax

c = 1 m/s) . . . . 59

16 Deployment surface area for unknown (a) and known (b) current directionβ, given a bottom target arealxh . . . . . . . . . . . . . . . . . . . . . . . 61

17 Three-dimensional scenario.3D coverage with a 3D random deployment . 65

18 Three-dimensional scenario.Optimized 3D coverage with a 2D bottom-random deployment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

19 Three-dimensional scenario.Optimized 3D coverage with a 2D bottom-grid deployment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

20 Theoretical and experimental sensing range . . . . . . . . . . . . . . . . . 66

ix

21 Theoretical, Fisher&Simon’s, and Thorp’s medium absorption coefficientα(f) vs. frequencyf ∈ [10−1, 102] kHz . . . . . . . . . . . . . . . . . . . 74

22 Single-packet transmission scheme . . . . . . . . . . . . . . . . . . . . . . 75

23 Underwater and terrestrial channel utilization efficiency for different dis-tances(100 m−500 m). Underwater channel efficiency vs. packet payloadsize without FEC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

24 Underwater and terrestrial channel utilization efficiency for different dis-tances(100 m−500 m). Underwater channel efficiency vs. packet payloadsize with(255, 239) Reed-Solomon FEC . . . . . . . . . . . . . . . . . . . 78

25 Underwater and terrestrial channel utilization efficiency for different dis-tances(100 m − 500 m). Terrestrial channel efficiency vs. packet payloadsize without FEC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

26 Packet-train performance.Packet-train transmission scheme . . . . . . . . 80

27 Packet-train performance.Underwater packet efficiency vs. packet pay-load size for different distances (100 m and500 m) . . . . . . . . . . . . . 84

28 Packet-train performance.Packet-train efficiency vs. packet-train payloadlength for different distances (100 m-500 m) . . . . . . . . . . . . . . . . . 85

29 Scenario 1: Delay-insensitive routing.Average node residual energy vs.time, for different link metrics . . . . . . . . . . . . . . . . . . . . . . . . 96

30 Scenario 1: Delay-insensitive routing.Average and standard deviation ofnumber of hops vs. time, for different link metrics . . . . . . . . . . . . . . 97

31 Scenario 1: Delay-insensitive routing.Average packet delay vs. time, fordifferent link metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

32 Scenario 1: Delay-insensitive routing.Distribution of data delivery delaysfor the Full Metric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

33 Scenario 1: Delay-insensitive routing.Average and standard deviationnode queueing delays, for different link metrics . . . . . . . . . . . . . . . 99

34 Scenario 1: Delay-insensitive routing.Average and standard deviation ofnumber of packet transmissions, for different link metrics . . . . . . . . . . 99

35 Scenario 2: Delay-insensitive routing.Packet delay and average delay vs.time for source rate equal to150 bps . . . . . . . . . . . . . . . . . . . . . 101



x

38 Scenario 3: Delay-sensitive routing.Packet delay and average delay vs.time for source rate equal to150 bps . . . . . . . . . . . . . . . . . . . . . 103



41 Scenario 3: Delay-sensitive routing.Generated, received, dropped, andlost traffic vs. time for source rate equal to150 bps . . . . . . . . . . . . . 104



44 Scenarios 2 and 3.Queue and average queue size vs. time; delay-insensitive,source rate equal to300 bps . . . . . . . . . . . . . . . . . . . . . . . . . . 106

45 Scenarios 2 and 3.Queue and average queue size vs. time; delay-insensitive,source rate equal to600 bps . . . . . . . . . . . . . . . . . . . . . . . . . . 106

46 Scenarios 2 and 3.Queue and average queue size vs. time; delay-sensitive,source rate equal to600 bps . . . . . . . . . . . . . . . . . . . . . . . . . . 107

47 Restoration of a native (a) and relayed connection (b) . . . . . . . . . . . . 112

48 Expected energy consumption for primary and backup paths . . . . . . . . 116

49 Average number of hops for primary and backup paths (optimal and short-est path) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

50 Energy consumption for primary and backup path (optimal and minimum-hop path) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

51 Generated, received, dropped, and lost traffic vs. time (50 nodes) . . . . . . 118

52 Average and surface station used energy per received bit vs. time (50 nodes) 119

53 Packet delay and average delay vs. time (50 nodes) . . . . . . . . . . . . . 119

54 Number of packets collided, duplicated, and corrupted (due to channel im-pairments) vs. time (50 nodes) . . . . . . . . . . . . . . . . . . . . . . . . 120

55 Queue and average queue size vs. time (50 nodes) . . . . . . . . . . . . . . 120

56 Expected routing energy increase due to sensor failure vs. time (50 nodes) . 121

57 Data and broadcast message transmissions . . . . . . . . . . . . . . . . . . 129

58 Minimum energy per bit vs. code length (Rayleigh Channel) . . . . . . . . 135

xi

59 2D Deep Water UW-ASNs.Average packet delay vs. simulation time (30sensors,6 uw-gateways) . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

60 2D Deep Water UW-ASNs.Average energy per received bit vs. simulationtime (30 sensors,6 uw-gateways) . . . . . . . . . . . . . . . . . . . . . . . 140

61 2D Deep Water UW-ASNs.Average packet delay vs. number of sensors . . 141

62 2D Deep Water UW-ASNs.Average normalized used energy vs. number ofsensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

63 2D Deep Water UW-ASNs.Normalized successfully received packets vs.number of sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

64 2D Deep Water UW-ASNs.Number of data packet collisions vs. numberof sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

65 3D Shallow Water UW-ASNs.Average packet delay vs. simulation time(30 sensors) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

66 3D Shallow Water UW-ASNs.Average energy per received bit vs. simula-tion time (30 sensors) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

67 3D Shallow Water UW-ASNs.Average packet delay vs. number of sensors . 144

68 3D Shallow Water UW-ASNs.Average normalized used energy vs. numberof sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

69 3D Shallow Water UW-ASNs.Normalized successfully received packetsvs. number of sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

70 3D Shallow Water UW-ASNs.Number of data packet collisions vs. numberof sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

71 3D UW-ASNs with mobile AUVs.Average packet delay vs. simulation time(30 sensors) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

72 3D UW-ASNs with mobile AUVs.Average energy per received bit vs. sim-ulation time (30 sensors) . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

73 3D UW-ASNs with mobile AUVs.Average packet delay vs. number of sensors148

74 3D UW-ASNs with mobile AUVs.Average normalized used energy vs.number of sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

75 3D UW-ASNs with mobile AUVs.Normalized successfully received packetsvs. number of sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

76 3D UW-ASNs with mobile AUVs.Number of data packet collisions vs.number of sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

xii

SUMMARY

Underwater sensor networks find applications in oceanographic data collection,

pollution monitoring, offshore exploration, disaster prevention, assisted navigation, tactical

surveillance, and mine reconnaissance. The enabling technology for these applications is

acoustic wireless networking. UnderWater Acoustic Sensor Networks (UW-ASNs) consist

of sensors and Autonomous Underwater Vehicles (AUVs) deployed to perform collabora-

tive monitoring tasks. The objective of this research is to explore fundamental key aspects

of underwater acoustic communications, propose communication architectures for UW-

ASNs, and develop efficient sensor communication protocols tailored for the underwater

environment. Specifically, different deployment strategies for UW-ASNs are studied, and

statistical deployment analysis for different architectures is provided. Moreover, a model

characterizing the underwater acoustic channel utilization efficiency is introduced. The

model allows setting the optimal packet size for underwater communications. Two distrib-

uted routing algorithms are proposed for delay-insensitive and delay-sensitive applications.

The proposed routing solutions allow each node to select its next hop, with the objective of

minimizing the energy consumption taking the different application requirements into ac-

count. In addition, a resilient routing solution to guarantee survivability of the network

to node and link failures in long-term monitoring missions is developed. Moreover, a

distributed Medium Access Control (MAC) protocol for UW-ASNs is proposed. It is a

transmitter-based code division multiple access scheme that incorporates a novel closed-

loop distributed algorithm to set the optimal transmit power and code length. It aims at

achieving high network throughput, low channel access delay, and low energy consump-

tion. Finally, an efficient cross-layer communication solution tailored for multimedia traffic

(i.e., video and audio streams, still images, and scalar sensor data) is introduced.

xiii

CHAPTER I

INTRODUCTION

1.1 Background

Underwater sensor networks are envisioned to enable applications for oceanographic data

collection, pollution monitoring, offshore exploration, disaster prevention, seismic mon-

itoring, equipment monitoring, assisted navigation and tactical surveillance applications.

Multiple Unmanned or Autonomous Underwater Vehicles (UUVs, AUVs), equipped with

underwater sensors, will also find application in exploration of natural undersea resources

and gathering of scientific data in collaborative monitoring missions. To make these ap-

plications viable, there is a need to enable underwater communications among underwater

devices. Underwater sensor nodes and vehicles must possess self-configuration capabil-

ities, i.e., they must be able to coordinate their operation by exchanging configuration,

location and movement information, and to relay monitored data to an onshore station.

Wireless underwater acoustic networking is the enabling technology for these applica-

tions. UnderWater Acoustic Sensor Networks (UW-ASNs) consist of a variable number

of sensors and vehicles that are deployed to perform collaborative monitoring tasks over a

given volume of mater. To achieve this objective, sensors and vehicles self-organize in an

autonomous network, which can adapt to the characteristics of the ocean environment.

The above described features enable a broad range of applications for underwater acoustic

sensor networks:

• Ocean Sampling Networks. Networks of sensors and AUVs, such as the Odyssey-

class AUVs, can perform synoptic, cooperative adaptive sampling of the 3D coastal

ocean environment. Experiments such as the Monterey Bay field experiment demon-

strated the advantages of bringing together sophisticated new robotic vehicles with

1

advanced ocean models to improve the ability to observe and predict the characteris-

tics of the oceanic environment.

• Environmental Monitoring. UW-ASN can perform pollution monitoring (chemi-

cal, biological, and nuclear). For example, it may be possible to detail the chemical

slurry of antibiotics, estrogen-type hormones and insecticides to monitor streams,

rivers, lakes, and ocean bays (water quality in-situ analysis) [95]. In addition, UW-

ASNs can perform ocean current and wind monitoring, and biological monitoring

such as tracking of fish or micro-organisms. Also, UW-ASNs can improve weather

forecast, detect climate change, and understand and predict the effect of human ac-

tivities on marine ecosystems. For example, in [97], the design and construction of a

simple underwater sensor network is described to detect extreme temperature gradi-

ents (thermoclines), which are considered to be a breeding ground for certain marine

microorganisms.

• Undersea Explorations.Underwater sensor networks can help detecting underwa-

ter oilfields or reservoirs, determine routes for laying undersea cables, and assist in

exploration for valuable minerals.

• Disaster Prevention. Sensor networks that measure seismic activity from remote

locations can providetsunamiwarnings to coastal areas [79], or study the effects of

submarine earthquakes (seaquakes).

• Seismic Monitoring. Frequent seismic monitoring is of great importance in oil ex-

traction from underwater fields to asses field performance. Underwater sensor net-

works would allow reservoir management approaches.

• Equipment Monitoring. Sensor networks would enable remote control and tempo-

rary monitoring of expensive equipment immediately after the deployment, to assess

deployment failures in the initial operation or to detect problems.

2

• Assisted Navigation.Sensors can be used to identify hazards on the seabed, locate

dangerous rocks or shoals in shallow waters, mooring positions, submerged wrecks,

and to perform bathymetry profiling.

• Distributed Tactical Surveillance. AUVs and fixed underwater sensors can col-

laboratively monitor areas forsurveillance, reconnaissance, targeting, andintrusion

detectionsystems. For example, in [16], a 3D underwater sensor network is designed

for a tactical surveillance system that is able to detect and classify submarines, Small

Delivery Vehicles (SDVs) and divers based on the sensed data from mechanical, ra-

diation, magnetic, and acoustic microsensors. With respect to traditional radar/sonar

systems, underwater sensor networks can reach a higher accuracy, and enable de-

tection and classification of low signature targets by also combining measures from

different types of sensors.

• Mine Reconnaissance. The simultaneous operation of multiple AUVs with acoustic

and optical sensors can be used to perform rapid environmental assessment and detect

mine-like objects.

Underwater networking is a rather unexplored area although underwater communica-

tions have been experimented since World War II, when, in1945, an underwater telephone

was developed in the United States to communicate with submarines [71]. Acoustic com-

munications are the typical physical layer technology in underwater networks. In fact,

radio waves propagate at long distances through conductive salty water only at extra low

frequencies(30− 300 Hz), which require large antennae and high transmission power. For

example, the Berkeley Mica2 Motes, the most popular experimental platform in the sen-

sor networking community, have been reported to have a transmission range of120 cm in

underwater at433 MHz by experiments performed at the Robotic Embedded Systems Lab-

oratory (RESL) at the University of Southern California. Optical waves do not suffer from

3

such high attenuation but are affected by scattering. Moreover, transmission of optical sig-

nals requires high precision in pointing the narrow laser beams. Thus, links in underwater

networks are based onacoustic wireless communications[82].

The traditional approach for ocean-bottom or ocean-column monitoring is to deploy

underwater sensors that record data during the monitoring mission, and then recover the

instruments [69]. This approach has the following disadvantages:

• No real-time monitoring. The recorded data cannot be accessed until the instru-

ments are recovered, which may happen several months after the beginning of the

monitoring mission. This is critical especially in surveillance or in environmental

monitoring applications such as seismic monitoring.

• No on-line system reconfiguration. Interaction between onshore control systems

and the monitoring instruments is not possible. This impedes any adaptive tuning

of the instruments, nor is it possible to reconfigure the system after particular events

occur.

• No failure detection. If failures or misconfigurations occur, it may not be possible to

detect them before the instruments are recovered. This can easily lead to the complete

failure of a monitoring mission.

• Limited Storage Capacity. The amount of data that can be recorded during the

monitoring mission by every sensor is limited by the capacity of the onboard storage

devices (memories, hard disks).

Therefore, there is a need to deploy underwater networks that will enable real-time

monitoring of selected ocean areas, remote configuration and interaction with onshore hu-

man operators. This can be obtained by connecting underwater instruments by means of

wireless links based on acoustic communication.

Many researchers are currently engaged in developing networking solutions for terres-

trial wireless ad hoc and sensor networks. Although there exist many recently developed

4

network protocols for wireless sensor networks, the unique characteristics of the under-

water acoustic communication channel, such as limited bandwidth capacity and variable

delays [70], require very efficient and reliable new data communication protocols.

Major challenges in the design of underwater acoustic networks are:

• The available bandwidth is severely limited;

• The underwater channel is impaired because of multi-path and fading;

• Propagation delay in underwater is five orders of magnitude higher than in Radio

Frequency (RF) terrestrial channels, and variable;

• High bit error rates and temporary losses of connectivity (shadow zones) can be

experienced;

• Underwater sensors are characterized by high cost because of a small relative number

of suppliers (i.e., not much economy of scale);

• Battery power is limited and usually batteries can not be recharged, also because

solar energy cannot be exploited;

• Underwater sensors are prone to failures because of fouling and corrosion.

1.2 Organization of the Thesis

This thesis is organized in eight chapters.

In Chapter 2, several fundamental key aspects of underwater acoustic communications

are investigated. Different architectures for two-dimensional and three-dimensional under-

water sensor networks are discussed, and the underwater channel is characterized. The

main challenges for the development of efficient networking solutions posed by the under-

water environment are detailed and a cross-layer approach to the integration of all commu-

nication functionalities is suggested. Furthermore, open research issues are discussed and

possible solution approaches are outlined.

5

In Chapter 3, different deployment strategies for two-dimensional and three-dimensional

communication architectures for UnderWater Acoustic Sensor Networks (UW-ASNs) are

proposed, and statistical deployment analysis for both architectures is provided. The ob-

jectives of this chapter are to determine the minimum number of sensors needed to be

deployed to achieve the optimal sensing and communication coverage, which are dictated

by the application; provide guidelines on how to choose the optimal deployment surface

area, given a target region; study the robustness of the sensor network to node failures, and

provide an estimate of the number of redundant sensors to be deployed to compensate for

possible failures.

In Chapter 4, a model characterizing the acoustic channel utilization efficiency is in-

troduced, which allows investigating some fundamental characteristics of the underwater

environment. In particular, the model allows setting the optimal packet size for underwater

communications given monitored volume, density of the sensor network, and application

requirements. Moreover, the problem of data gathering is investigated at the network layer

by considering the cross-layer interactions between the routing functions and the character-

istics of the underwater acoustic channel. Two distributed routing algorithms are introduced

for delay-insensitive and delay-sensitive applications. The proposed solutions allow each

node to select its next hop, with the objective of minimizing the energy consumption taking

the varying condition of the underwater channel and the different application requirements

into account. The proposed routing solutions are shown to achieve the performance targets

by means of simulation.

In Chapter 5, the problem of data gathering for three-dimensional underwater sensor

networks is investigated at the network layer by considering the interactions between the

routing functions and the characteristics of the underwater acoustic channel. A two-phase

resilient routing solution for long-term monitoring missions is developed, with the objec-

tive of guaranteeing survivability of the network to node and link failures. In the first phase,

6

energy-efficient node-disjoint primary and backup paths are optimally configured, by rely-

ing on topology information gathered by a surface station. In the second phase, paths are

locally repaired in case of node failures.

In Chapter 6, UW-MAC, a distributed Medium Access Control (MAC) protocol tailored

for UnderWater Acoustic Sensor Networks (UW-ASNs), is proposed. It is a transmitter-

based Code Division Multiple Access (CDMA) scheme that incorporates a novel closed-

loop distributed algorithm to set the optimal transmit power and code length. UW-MAC

aims at achieving three objectives, i.e., guarantee high network throughput, low channel

access delay, and low energy consumption. It is proven that UW-MAC manages to si-

multaneously achieve the three objectives in deep water communications, which are not

severely affected by multipath. In shallow water communications, which may be heavily

affected by multipath, it dynamically finds the optimal trade-off among these objectives,

depending on the application requirements. UW-MAC is the first protocol that leverages

CDMA properties to achieve multiple access to the scarce underwater bandwidth, while ex-

isting papers considered CDMA only from a physical layer perspective. Experiments show

that UW-MAC outperforms existing MAC protocols tuned for the underwater environment

under different architecture scenarios and simulation settings.

In Chapter 7, a cross-layer resource allocation problem is formulated in multi-hop wire-

less underwater networks as an optimization problem. While we first outline a general

framework where different resource allocation problems will fit by specifying the form of

particular functions, then we specialize the framework for the underwater environment.

Finally, Chapter 8 concludes the thesis.

7

CHAPTER II

RESEARCH CHALLENGES FOR UNDERWATER ACOUSTIC

SENSOR NETWORKS

2.1 Preliminaries

In this chapter, we discuss several fundamental key aspects of underwater acoustic com-

munications. We discuss the communication architecture of underwater sensor networks as

well as the factors that influence underwater network design. The ultimate objective of this

work is to encourage research efforts to lay down fundamental bases for the development

of new advanced communication techniques for efficient underwater communication and

networking for enhanced ocean monitoring and exploration applications.

The remainder of this chapter is organized as follows. In Section 2.2 and 2.3 we intro-

duce the communication architectures and design challenges, respectively, of underwater

acoustic networks. In Section 2.4, we investigate the underwater acoustic communication

channel and summarize the associated physical layer challenges for underwater network-

ing. In Sections 2.5, 2.6, 2.7, 2.8, and 2.9, we discuss physical, data link, network, trans-

port, and application layer issues in underwater sensor networks, respectively. Finally, in

Section 2.10 we describe some experimental implementations of underwater sensor net-

works.

2.2 Communication Architectures

In this section, we describe the communication architectures of underwater acoustic sen-

sor networks. In particular, we introduce reference architectures for two-dimensional and

three-dimensional underwater networks, and present several types of AUVs that can en-

hance the capabilities of underwater sensor networks.

8

The network topology is in general a crucial factor in determining theenergy consump-

tion, thecapacity, and thereliability of a network. Hence, the network topology should

be carefully engineered and post-deploymenttopology optimizationshould be performed,

when possible.

Underwater monitoring missions can be extremely expensive because of the high cost

of underwater devices. Hence, it is important that the deployed network be highly reliable,

so as to avoid failure of monitoring missions due to failure of single or multiple devices.

For example, it is crucial to avoid designing the network topology with single points of

failure, which could compromise the overall functioning of the network.

The network capacity is also influenced by the network topology. Since the capacity of

the underwater channel is severely limited, as will be discussed in Section 2.4, it is very

important to organize the network topology in such a way that nocommunication bottleneck

is introduced.

The communication architectures introduced here are used as a basis for discussion

of the challenges associated with underwater acoustic sensor networks. The underwater

sensor network topology is an open research issue in itself that needs further analytical and

simulative investigation from the research community. In the remainder of this section, we

discuss the following architectures:

• Static two-dimensional UW-ASNs for ocean bottom monitoring. These are con-

stituted by sensor nodes that are anchored to the bottom of the ocean, as discussed in

Section 2.2.1. Typical applications may be environmental monitoring, or monitoring

of underwater plates in tectonics [30].

• Static three-dimensional UW-ASNs for ocean-column monitoring. These include

networks of sensors whose depth can be controlled by means of techniques discussed

in Section 2.2.2, and may be used for surveillance applications or monitoring of

ocean phenomena (ocean bio-geo-chemical processes, water streams, pollution).

9

Figure 1: Architecture for 2D underwater sensor networks

• Three-dimensional networks of Autonomous Underwater Vehicles (AUVs). These

networks include fixed portions composed of anchored sensors and mobile portions

constituted by autonomous vehicles, as detailed in Section 2.2.3.

2.2.1 Two-dimensional Underwater Sensor Networks

A reference architecture for two-dimensional underwater networks is shown in Fig. 1. A

group of sensor nodes are anchored to the bottom of the ocean with deep ocean anchors.

Underwater sensor nodes are interconnected to one or moreunderwater gateways(uw-

gateways) by means of wireless acoustic links. Uw-gateways, as shown in Fig. 1, are

network devices in charge of relaying data from the ocean bottom network to a surface sta-

tion. To achieve this objective, uw-gateways are equipped with two acoustic transceivers,

namely avertical and ahorizontal transceiver. The horizontal transceiver is used by the

uw-gateway to communicate with the sensor nodes to: i) send commands and configura-

tion data to the sensors (uw-gateway to sensors); and ii) collect monitored data (sensors to

uw-gateway). The vertical link is used by the uw-gateways to relay data to asurface sta-

tion. In deep water applications, vertical transceivers must be long range transceivers as the

ocean can be as deep as10 km. The surface station is equipped with an acoustic transceiver

10

that is able to handle multiple parallel communications with the deployed uw-gateways. It

is also endowed with a long range RF and/or satellite transmitter to communicate with the

onshore sink(os-sink) and/or to asurface sink(s-sink).

Sensors can be connected to uw-gateways via direct links or through multi-hop paths.

In the former case, each sensor directly sends the gathered data to the selected uw-gateway.

However, in UW-ASNs, the power necessary to transmit may decay with powers greater

than two of the distance [81], and the uw-gateway may be far from the sensor node. Con-

sequently, although direct link connection is the simplest way to network sensors, it may

not be the most energy efficient solution. Furthermore, direct links are very likely to re-

duce the network throughput because of increased acoustic interference caused by the high

transmission power. In case of multi-hop paths, as in terrestrial sensor networks [8], the

data produced by a source sensor is relayed by intermediate sensors until it reaches the

uw-gateway. This results in energy savings and increased network capacity, but increases

the complexity of the routing functionality as well. In fact, every network device usually

takes part in a collaborative process whose objective is to diffuse topology information

such that efficient and loop free routing decisions can be made at each intermediate node.

This process involves signaling and computation. Since energy and capacity are precious

resources in underwater environments, as discussed above, in UW-ASNs the objective is to

deliver event features by exploiting multi-hop paths and minimizing the signaling overhead

necessary to construct underwater paths at the same time.

2.2.2 Three-dimensional Underwater Sensor Networks

Three dimensional underwater networks are used to detect and observe phenomena that

can not be adequately observed by means of ocean bottom sensor nodes, i.e., to perform

cooperative sampling of the 3D ocean environment. In three-dimensional underwater net-

works, sensor nodes float at different depths to observe a given phenomenon. One possible

solution would be to attach each uw-sensor node to a surface buoy, by means of wires

11

Figure 2: Architecture for 3D underwater sensor networks

whose length can be regulated so as to adjust the depth of each sensor node [16]. However,

although this solution allows easy and quick deployment of the sensor network, multiple

floating buoys may obstruct ships navigating on the surface, or they can be easily detected

and deactivated by enemies in military settings. Furthermore, floating buoys are vulnerable

to weather and tampering or pilfering.

For these reasons, a different approach can be to anchor sensor devices to the bottom

of the ocean. In this architecture, depicted in Fig. 2, each sensor is anchored to the ocean

bottom and equipped with a floating buoy that can be inflated by a pump. The buoy pushes

the sensor towards the ocean surface. The depth of the sensor can then be regulated by

adjusting the length of the wire that connects the sensor to the anchor, by means of an

electronically controlled engine that resides on the sensor. A challenge to be addressed in

such an architecture is the effect of ocean currents on the described mechanism to regulate

the depth of the sensors.

Many challenges arise with such an architecture, that need to be solved to enable 3D

monitoring, including:

• Sensing coverage. Sensors should collaboratively regulate their depth in order to

12

Figure 3: Architecture for 3D underwater sensor networks with AUVs

achieve 3D coverage of the ocean column, according to their sensing ranges. Hence,

it must be possible to obtain sampling of the desired phenomenon at all depths.

• Communication coverage. Since in 3D underwater networks there may be no notion

of uw-gateway, sensors should be able to relay information to the surface station via

multi-hop paths. Thus, network devices should coordinate their depths in such a way

that the network topology be always connected, i.e., at least one path from every

sensor to the surface station always exists.

Sensing and communication coverage in a 3D environment are rigorously investigated

in [74]. The diameter, minimum and maximum degree of the reachability graph that de-

scribes the network are derived as a function of the communication range, while different

degrees of coverage for the 3D environment are characterized as a function of the sensing

range. These techniques could be exploited to investigate the coverage issues in UW-ASNs.

2.2.3 Sensor Networks with Autonomous Underwater Vehicles

AUVs can function without tethers, cables, or remote control, and therefore they have

a multitude of applications in oceanography, environmental monitoring, and underwater

13

resource study. Previous experimental work has shown the feasibility of relatively inex-

pensive AUV submarines equipped with multiple underwater sensors that can reach any

depth in the ocean. Hence, they can be used to enhance the capabilities of underwater sen-

sor networks in many ways. Figure 3 shows a reference architecture for 3D underwater

sensor networks with AUVs. The integration and enhancement of fixed sensor networks

with AUVs is an almost unexplored research area that requires new network coordination

algorithms such as:

• Adaptive sampling. This includes control strategies to command the mobile vehi-

cles to places where their data will be most useful. This approach is also known as

adaptive samplingand has been proposed in pioneering monitoring missions. For ex-

ample, the density of sensor nodes can be adaptively increased in a given area when

a higher sampling rate is needed for a given monitored phenomenon.

• Self-Configuration. This includes control procedures to automatically detect con-

nectivity holes caused by node failures or channel impairment and request the in-

tervention of an AUV. Furthermore, AUVs can either be used for installation and

maintenance of the sensor network infrastructure or to deploy new sensors. They can

also be used as temporary relay nodes to restore connectivity.

One of the design objectives of AUVs is to make them rely on local intelligence, and be

less dependent on communications from online shores [38]. In general, control strategies

are needed for autonomous coordination, obstacle avoidance, and steering strategies. Solar

energy systems allow increasing the lifetime of AUVs, i.e., it is not necessary to recover and

recharge the vehicle on a daily basis. Hence, solar powered AUVs can acquire continuous

information for periods of time of the order of months [41].

14

Several types of AUVs exist as experimental platforms for underwater experiments.

Some of them resemble small-scale submarines (such as the Odyssey-class AUVs devel-

oped at MIT). Others are simpler devices that do not encompass such sophisticated ca-

pabilities. For example,drifters andgliders are oceanographic instruments often used in

underwater explorations. Drifter underwater vehicles drift with local current and have the

ability to move vertically through the water column, and are used for taking measurements

at preset depths [37]. Underwater gliders [23] are battery powered autonomous underwater

vehicles that use hydraulic pumps to vary their volume by a few hundred cubic centimeters

to generate the buoyancy changes that power their forward gliding. When they emerge on

the surface, Global Positioning System (GPS) is used to locate the vehicle. This informa-

tion can be relayed to the onshore station while operators can interact by sending control

information to the gliders. Depth capabilities range from200 m to 1500 m while operating

lifetimes range from a few weeks to several months. These long durations are possible

because gliders move very slowly, typically25 cm/s (0.5 knots). In [62], a control strategy

for groups of gliders to cooperatively move and reconfigure in response to a sensed distrib-

uted environment is presented. The proposed framework allows preserving the symmetry

of the group of gliders. The group is constrained to maintain a uniform distribution as

needed, but is free to spin and possibly wiggle with current. In [27], results are reported on

the application of the theory in [62] on a fleet of autonomous underwater gliders during the

experiment on Monterey Bay in 2003.

2.3 Design Challenges

In this section, we describe the design challenges of underwater acoustic sensor networks.

In particular, we itemize the main differences between terrestrial and underwater sensor

networks, we detail key design issues and deployment challenges for underwater sensors,

and we give motivations for cross-layer design approach to improve the network efficiency

in the critical underwater environment.

15

2.3.1 Differences with Terrestrial Sensor Networks

The main differences between terrestrial and underwater sensor networks can be outlined

as follows:

• Cost. While terrestrial sensor nodes are expected to become increasingly inexpen-

sive, underwater sensors are expensive devices. This is especially due to the more

complex underwater transceivers and to the hardware protection needed in the ex-

treme underwater environment. Also, because of the low economy of scale caused

by a small relative number of suppliers, underwater sensors are characterized by high

cost.

• Deployment.While terrestrial sensor networks are densely deployed, in underwater,

the deployment is generally more sparse.

• Power. The power needed for acoustic underwater communications is higher than in

terrestrial radio communications because of the different physical layer technology

(acoustic vs. RF waves), the higher distances, and more complex signal processing

techniques implemented at the receivers to compensate for the impairments of the

channel.

• Memory. While terrestrial sensor nodes have very limited storage capacity, uw-

sensors may need to be able to do some data caching as the underwater channel may

be intermittent.

• Spatial Correlation. While the readings from terrestrial sensors are often correlated,

this is more unlikely to happen in underwater networks due to the higher distance

among sensors.

2.3.2 Underwater Sensors

The typical internal architecture of an underwater sensor is shown in Fig. 4. It consists

of a main controller/CPU, which is interfaced with an oceanographic instrument or sensor

16

Figure 4: Internal organization of an underwater sensor node

through a sensor interface circuitry. The controller receives data from the sensor and it can

store it in the onboard memory, process it, and send it to other network devices by control-

ling the acoustic modem. The electronics are usually mounted on a frame that is protected

by a PVC housing. Sometimes all sensor components are protected by bottom-mounted

instrument frames that are designed to permit azimuthally omnidirectional acoustic com-

munications, and protect sensors and modems from potential impact of trawling gear, es-

pecially in areas subjected to fishing activities. In [20], the protecting frame is designed

so as to deflect trawling gear on impact, by housing all components beneath a low-profile

pyramidal frame.

Underwater sensing devices include sensors to measure the quality of water and to

study its characteristics such as temperature, density, salinity (interferometric and refrac-

tometric sensors), acidity, chemicals, conductivity, pH (magnetoelastic sensors), oxygen

(Clark-type electrode), hydrogen, dissolved methane gas (METS), and turbidity. Dispos-

able sensors exist that detect ricin, the highly poisonous protein found in castor beans and

thought to be a potential terrorism agent. DNA microarrays can be used to monitor both

17

abundance and activity level variations among natural microbial populations. Other ex-

isting underwater sensors include hydrothermal sulfide, silicate, voltammetric sensors for

spectrophotometry, gold-amalgam electrode sensors for sediment measurements of metal

ions (ion-selective analysis), amperometric microsensors for H2S measurements for stud-

ies of anoxygenic photosynthesis, sulfide oxidation, and sulfate reduction of sediments. In

addition, force/torque sensors for underwater applications requiring simultaneous measure-

ments of several forces and moments have also been developed, as well as quantum sensors

to measure light radiation and sensors for measurements of harmful algal blooms.

The challenges related to the deployment of low cost, low scale underwater sensors, are

listed below:

• It is necessary to develop less expensive, robust “nano-sensors”, e.g., sensors based

on Nano-Technology, which involves development of materials and systems at the

atomic, molecular, or macromolecular levels in the dimension range of approxi-

mately1− 500 nm.

• It is necessary to devise periodical cleaning mechanisms against corrosion and foul-

ing, which may impact the lifetime of underwater devices. For example, some sen-

sors for pCO2, pH and nitrate measurement, and fluorometers and spectral radiome-

ters, may be limited by bio-fouling, especially on a long time scale.

• There is a need for robust, stable sensors on a high range of temperatures since sensor

drift of underwater devices may be a concern. To this end, protocols forin situ

calibrations of sensors to improve accuracy and precision of sampled data must be

developed.

• There is a need for new integrated sensors forsynopticsampling of physical, chem-

ical, and biological parameters to improve the understanding of processes in marine

systems.

18

2.3.3 A Cross-layer Protocol Stack

A protocol stack for uw-sensors should combinepower awarenessandmanagement, and

promotecooperationamong the sensor nodes. It should consist ofphysical layer, data link

layer, network layer, transport layer, andapplication layerfunctionalities. The protocol

stack should also include apower management plane, acoordination plane, and alocaliza-

tion plane. The power management plane is responsible for network functionalities aimed

at minimizing the energy consumption (e.g., sleep modes, power control). The coordina-

tion plane is responsible for all functionalities that require coordination among sensors,

(e.g., coordination of the sleep modes, data aggregation, 3D topology optimization). The

localization plane is responsible for providing absolute or relative localization information

to the sensor node, when needed by the protocol stack or by the application.

While all the research on underwater networking so far has followed the traditional

layered approach for network design, it is an increasingly accepted opinion in the wireless

networking community that the improved network efficiency, especially in critical environ-

ments, can be obtained with a cross-layer design approach. These techniques will entail

a joint design of different network functionalities, from modem design to MAC and rout-

ing, from channel coding and modulation to source compression and transport layer, with

the objective to overcome the shortcomings of a layered approach that lacks of informa-

tion sharing across protocol layers, forcing the network to operate in a suboptimal mode.

Hence, while in the following sections for the sake of clarity we present the challenges

associated with underwater sensor networks following the traditional layered approach, we

believe that the underwater environment particularly requires for cross-layer design solu-

tions that allow a more efficient use of the scarce available resources. However, although

we advocate integrating functionalities to improve network performance and to avoid du-

plication of functions by means of cross-layer design, it is important to consider the ease of

design by following amodular design approach. This also allows improving and upgrading

particular functionalities without the need to re-design the entire communication system.

19

Although systematic research on cross-layer design for underwater communications

is missing, a study on the interaction between physical and MAC layers is presented in

[43], where a method is proposed based on the sonar equation [90] to estimate the battery

lifetime and power cost for shallow water1 underwater acoustic sensor networks for civilian

applications. The battery lifetime is modeled as dependent on four key parameters, namely

internode distance, transmission frequency, frequency of data updates and number of nodes

per cluster. Interestingly, since in shallow water the acoustic propagation loss increases

with increasing frequency and distance (as shown in Fig. 5), it is proposed to assign lower

frequencies to sensor nodes that are closer to the sink, since they also have to relay data on

behalf of more distant nodes. This way, the energy consumption is somehow equalized and

the network lifetime is prolonged.

2.3.4 Real-time Networking vs. Delay Tolerant Networking

As in terrestrial sensor networks, depending on the application there may be very different

requirements for data delivery. For example, surveillance application may need very fast

reaction to events and thus networking protocols that provide guaranteed delay-bounded

delivery are required. Hence, it is necessary to develop protocols that deal with the charac-

teristics of the underwater environment to quickly restore connectivity when lost and that

react to unpaired or congested links by taking appropriate action (e.g., dynamical rerouting)

to meet the given delay bound. Conversely, other applications may produce large bundles

of data to be delivered to the onshore sink without particular delay constraints. With this

respect, the Delay-Tolerant Networking Research Group (DTNRG) [26] developed mech-

anisms to resolve the intermittent connectivity, long or variable delay, asymmetric data

rates, and high error rates by using astore and forwardmechanism based on a middleware

between the application layer and the lower layers. Similar methodologies may be partic-

ularly useful for applications such as those that record seismic activity, which have very

1In oceanic literature,shallow waterrefers to water with depth lower than100m, while deep waterisused for deeper oceans.

20

Table 1: Available bandwidth for different ranges in UW-A channels

Range[km] Bandwidth [kHz]Very Long 1000 < 1

Long 10− 100 2− 5Medium 1− 10 ≈ 10

Short 0.1− 1 20− 50Very Short < 0.1 > 100

low duty cycle and produce, when activated, large bundles of data that need to be relayed

to a monitoring station where it can be analyzed to predict future activity. On the other

hand, sensor networks intended for disaster prevention such as those that provide earth-

quake or tsunami warnings, require immediate delivery of information and hence real-time

protocols. Therefore, the design of networking solutions for underwater acoustic sensor

networks should always be aware of the difference between real-time and delay tolerant

(and delay-sensitive and delay-insensitive) applications, and jointly tune existing solutions

to the application needs and to the characteristics of the underwater environment.

2.4 Basics of Underwater Acoustic Propagation

Underwater acoustic communications are mainly influenced bypath loss, noise, multi-path,

Doppler spread, andhigh and variable propagation delay. All these factors determine the

temporal and spatial variabilityof the acoustic channel, and make the available bandwidth

of theUnderWater Acoustic channel(UW-A) limited and dramatically dependent on both

range and frequency. Long-range systems that operate over several tens of kilometers may

have a bandwidth of only a few kHz, while a short-range system operating over several tens

of meters may have more than a hundred kHz of bandwidth. In both cases these factors lead

to low bit rate [15], in the order of tens of kbit/s for existing devices.

Underwater acoustic communication links can be classified according to their range as

very long, long, medium, short, andvery shortlinks [82]. Table 1 shows typical bandwidths

of the underwater channel for different ranges. Acoustic links are also roughly classified

21

0200

400600

8001000

0

10

20

30

40

5020

30

40

50

60

70

80

90

Distance [m]

Path Loss vs Distance and Frequency in Band 1−50 KHz

Frequency [KHz]

Aco

ustic

Pow

er A

ttenu

atio

n [d

B]

Figure 5: Path loss of short range shallow UW-A channels vs. distance and frequency inband1− 50 kHz

asvertical andhorizontal, according to the direction of the sound ray with respect to the

ocean bottom. As will be shown later their propagation characteristics differ considerably,

especially with respect to time dispersion, multi-path spreads, and delay variance. In the

following, as usually done in oceanic literature,shallow waterrefers to water with depth

lower than100 m, while deep wateris used for deeper oceans.

Hereafter we analyze the factors that influence acoustic communications in order to

state the challenges posed by the underwater channels for underwater sensor networking.

These include:

• Path loss

– Attenuation. Is mainly provoked by absorption caused by the conversion of

acoustic energy into heat. The attenuation increases with distance and fre-

quency. Figure 5 shows the acoustic attenuation with varying frequency and

distance for a short range shallow water UW-A channel, according to the prop-

agation model in [90]. The attenuation is also caused by scattering and rever-

beration (on rough ocean surface and bottom), refraction, and dispersion (due to

22

the displacement of the reflection point caused by wind on the surface). Water

depth plays a key role in determining the attenuation.

– Geometric Spreading. This refers to the spreading of sound energy as a result

of the expansion of the wavefronts. It increases with the propagation distance

and is independent of frequency. There are two common kinds of geometric

spreading:spherical(omni-directional point source), which characterizes deep

water communications, andcylindrical (horizontal radiation only), which char-

acterizes shallow water communications.

• Noise

– Man made noise.This is mainly caused by machinery noise (pumps, reduction

gears, power plants), and shipping activity (hull fouling, animal life on hull,

cavitation), especially in areas encumbered with heavy vessel traffic.

– Ambient Noise. Is related to hydrodynamics (movement of water including

tides, current, storms, wind, and rain), and to seismic and biological phenom-

ena. In [34], boat noise and snapping shrimps have been found to be the primary

sources of noise in shallow water by means of measurement experiments on the

ocean bottom.

• Multi-path

– Multi-path propagation may be responsible for severe degradation of the acoustic

communication signal, since it generates Inter Symbol Interference (ISI).

– The multi-path geometry depends on the link configuration. Vertical channels

are characterized by little time dispersion, whereas horizontal channels may

have extremely long multi-path spreads.

– The extent of the spreading is a strong function of depth and the distance be-

tween transmitter and receiver.

23

• High delay and delay variance

– The propagation speed in the UW-A channel is five orders of magnitude lower

than in the radio channel. This large propagation delay (0.67 s/km) can reduce

the throughput of the system considerably.

– The high delay variance is even more harmful for efficient protocol design, as it

prevents from accurately estimating the Round Trip Time (RTT), which is the

key parameter for many common communication protocols.

• Doppler spread

– The Doppler frequency spread can be significant in UW-A channels [82], caus-

ing a degradation in the performance of digital communications: transmissions

at a high data rate cause many adjacent symbols to interfere at the receiver,

requiring sophisticated signal processing to deal with the generated ISI.

– The Doppler spreading generates a simple frequency translation, which is rel-

atively easy for a receiver to compensate for; and a continuous spreading of

frequencies, which constitutes a non-shifted signal, which is more difficult to

compensate for.

– If a channel has a Doppler spread with bandwidthB and a signal has sym-

bol durationT , then there are approximatelyBT uncorrelated samples of its

complex envelope. WhenBT is much less than unity, the channel is said to

beunderspreadand the effects of the Doppler fading can be ignored, while, if

greater than unity, it is said to beoverspread[48].

2.5 Physical Layer

Until the beginning of the last decade, due to the challenging characteristics of the un-

derwater channel, underwater modem development was based onnon-coherentFrequency

24

Shift Keying (FSK) modulation, since it relies on energy detection and thus does not re-

quire phase tracking, which is a very difficult task mainly because of the Doppler-spread

in the UW-A channel, described in Section 2.4. In FSK modulation schemes developed for

underwater, the multi-path effects are suppressed by inserting time guards between succes-

sive pulses to ensure that the reverberation, caused by the rough ocean surface and bottom,

vanishes before each subsequent pulse is received. Dynamic frequency guards can also be

used between frequency tones to adapt the communication to the Doppler spreading of the

channel. Although non-coherent modulation schemes are characterized by a highpower ef-

ficiency, their lowbandwidth efficiencymakes them unsuitable for high data rate multiuser

networks. Hence,coherent modulationtechniques have been developed for long-range,

high-throughput systems. In the last years,fully coherent modulation techniques, such as

Phase Shift Keying (PSK) and Quadrature Amplitude Modulation (QAM), have become

practical because of the availability of powerful digital processing. Channel equalization

techniques are exploited to leverage the effect of the Inter Symbol Interference (ISI), in-

stead of trying to avoid or suppress it. Decision Feedback Equalizers (DFE) track the com-

plex, relatively slowly varying channel response and thus provide high throughput when

the channel is slowly varying. Conversely, when the channel varies faster, it is necessary to

combine the DFE with a Phase Locked Loop (PLL) [84], which estimates and compensates

for the phase offset in a rapid, stable manner. The use of decision feedback equalization

and phase-locked loops is driven by the complexity and time variability of ocean channel

impulse responses. Table 2 presents the evolution from non-coherent modems to the recent

coherent modems.

Differential Phase Shift Keying (DPSK) serves as an intermediate solution between

incoherent and fully coherent systems in terms of bandwidth efficiency. DPSK encodes

information relative to the previous symbol rather than to an arbitrary fixed reference in the

signal phase and may be referred to as apartially coherent modulation. While this strat-

egy substantially alleviates carrier phase-tracking requirements, the penalty is an increased

25

Table 2: Evolution of modulation technique

Type Year Rate[ kbps] Band [kHz] Range[ km]FSK 1984 1.2 5 3s

PSK 1989 500 125 0.06d

FSK 1991 1.25 10 2d

PSK 1993 0.3− 0.5 0.3− 1 200d − 90s

PSK 1994 0.02 20 0.9s

FSK 1997 0.6− 2.4 5 10d − 5s

DPSK 1997 20 10 1d

PSK 1998 1.67− 6.7 2− 10 4d − 2s

16-QAM 2001 40 10 0.3s

* The subscriptsd ands stand fordeepandshallowwater

error probability over PSK at an equivalent data rate.

With respect to Table 2, it is worth noticing that early phase-coherent systems achieved

higher bandwidth efficiencies (bit rate/occupied bandwidth) than their incoherent counter-

parts, but they did not outperform incoherent modulation schemes yet. In fact, coherent

systems had lower performance than incoherent systems for long-haul transmissions on

horizontal channels until ISI compensation via decision-feedback equalizers for optimal

channel estimation was implemented [85]. However, these filtering algorithms are complex

and not suitable for real-time communications, as they do not meet real-time constraints.

Hence, sub-optimal filters have to be considered, but the imperfect knowledge of the chan-

nel impulse response that they provide leads to channel estimation errors, and ultimately to

decreased performance.

Another promising solution for underwater communications is the Orthogonal Fre-

quency Division Multiplexing (OFDM) spread spectrum technique, which is particularly

efficient when noise is spread over a large portion of the available bandwidth. OFDM is

frequently referred to as multi-carrier modulation because it transmits signals over multiple

sub-carrierssimultaneously. In particular, sub-carriers which experience higher Signal-to-

Noise Ratio (SNR), are allotted with a higher number of bits, whereas less bits are allotted

26

to sub-carriers experiencing attenuation, according to the concept ofbit loading, which re-

quires channel estimation. Since the symbol duration for each individual carrier increases,

OFDM systems perform robustly in severe multi-path environments, and achieve a high

spectral efficiency.

Many of the techniques discussed above require underwater channel estimation, which

can be achieved by means of probe packets [44]. An accurate estimate of the channel can

be obtained with a high probing rate and/or with a large probe packet size, which however

result in high overhead, and in the consequent drain of channel capacity and energy.

2.5.1 Open Research Issues

To enable physical layer solutions specifically tailored for underwater acoustic sensor net-

works, the following open research issues need to be addressed:

• It is necessary to develop inexpensive transmitter/receiver modems for underwater

communications.

• Research is needed on design of low-complexity sub-optimal filters characterized by

rapid convergence to enable real-time underwater communications with decreased

energy expenditure.

• There is a need to overcome stability problem in the coupling between the Phase

Locked Loop (PLL) and the Decision Feedback Equalizer (DCE).

2.6 Data Link Layer

In this section, we discuss techniques for multiple access in UW-ASNs and present open

research issues to address the requirements of the data link layer in an underwater envi-

ronment. Channel access control in UW-ASNs poses additional challenges because of the

peculiarities of the underwater channel, in particular limited bandwidth, and high and vari-

able delay.

27

Frequency Division Multiple Access (FDMA) is not suitable for UW-ASNs due to the

narrow bandwidth in UW-A channels and the vulnerability of limited band systems to fad-

ing and multi-path.

Time Division Multiple Access (TDMA) shows a limited bandwidth efficiency because

of the long time guards required in the UW-A channel. In fact, long time guards must be

designed to account for the large propagation delay and delay variance of the underwater

channel, discussed in Section 2.4, to minimize packet collisions from adjacent time slots.

Moreover, the variable delay makes it very challenging to realize a precise synchronization,

with a common timing reference, which is required for TDMA.

Carrier Sense Multiple Access (CSMA) prevents collisions with the ongoing trans-

mission at the transmitter side. To prevent collisions at the receiver side, however, it is

necessary to add a guard time between transmissions dimensioned according to the max-

imum propagation delay in the network. This makes the protocol dramatically inefficient

for UW-ASNs.

The use of contention-based techniques that rely on handshaking mechanisms such

as RTS/CTS in shared medium access (e.g., MACA [45], IEEE 802.11) is impractical

in underwater, for the following reasons: i) large delays in the propagation of RTS/CTS

control packets lead to low throughput; ii) due to the high propagation delay of UW-A

channels, when carrier sense is used, as in 802.11, it is more likely that the channel be

sensed idle while a transmission is ongoing since the signal may not have reached the

receiver yet; iii) the high variability of delay in handshaking packets makes it impractical

to predict the start and finish time of the transmissions of other stations. Thus, collisions

are highly likely to occur.

Many novel access schemes have been designed for terrestrial sensor networks, whose

objective, similarly to underwater sensor networks, is to prevent collisions in the access

channel thus maximizing the network efficiency. These similarities would suggest to tune

and apply those efficient schemes in the underwater environment; on the other hand, the

28

main focus in medium access control in terrestrial wireless sensor networks is on energy-

latency tradeoffs. Some proposed schemes aim at decreasing the energy consumption by

using sleep schedules with virtual clustering. However, these techniques may not be suit-

able for an environment where dense sensor deployment cannot be assumed. Moreover, the

additional challenges in underwater channels such as variable and high propagation delays,

and very limited available bandwidth, further complicate the medium access problem in

underwater environments.

Code Division Multiple Access (CDMA) is quite robust to frequency selective fading

caused by underwater multi-paths, since it distinguishes simultaneous signals transmitted

by multiple devices by means of pseudo-noise codes that are used for spreading the user

signal over the entire available band. This allows exploiting the time diversity in the UW-A

channel by leveragingRake filters[80] at the receiver. These filters are designed to match

the pulse spreading, the pulse shape, and the channel impulse response, so as to compensate

for the effect of multi-path. CDMA allows reducing the number of packet retransmissions,

which results in decreased battery consumption and increased network throughput. For

example, in [31], two code-division spread-spectrum access techniques are compared in

shallow water, namely Direct Sequence Spread Spectrum (DSSS) and Frequency Hopping

Spread Spectrum (FHSS). Although FHSS is more prone to the Doppler shift effect, since

the transmission takes place in narrow bands, this scheme is more robust to Multiple Ac-

cess Interference (MAI) than DSSS. Furthermore, although FHSS is shown to lead to a

higher bit error rate than DHSS, it results in simple receivers and provides robustness to

the near-far problem, thus potentially simplifying the power control functionality. One of

the most attractive access techniques in the recent underwater literature combines multi

carrier transmission with the DSSS CDMA [44], as it may offer higher spectral efficiency

than its single carrier counterpart and increase the flexibility to support integrated high data

rate applications with different quality of service requirements. The main idea is to spread

each data symbol in the frequency domain by transmitting all the chips of a spread symbol

29

at the same time into a large number of narrow subchannels. This way, high data rate can

be supported by increasing the duration of each symbol, which drastically reduces ISI.

In conclusion, although the high delay spread that characterizes the horizontal link

in underwater channels makes it difficult to maintain synchronization among the stations,

especially when orthogonal code techniques are used [44], CDMA is a promising multiple

access technique for underwater acoustic networks, particularly in shallow water where

multi-paths and Doppler-spreading play a key role in the communication performance.

In [76], a protocol is proposed for networks with AUVs. The proposed scheme is based

on organizing the network in multiple clusters, each composed of adjacent vehicles. Inside

each cluster, TDMA is used with long band guards, to overcome the effect of propagation

delay in underwater. In this case, TDMA is not highly inefficient since vehicles in the

same cluster are close to one another. Hence, the effect of propagation delay is limited.

Interference among different clusters is avoided by assigning different spreading codes to

different clusters. The proposed protocol sketches also some mechanisms to reorganize

clusters after node mobility.

In order to meet a required bit error rate at the data link layer of the deployed underwater

sensor networks, it is mandatory to provide error control functionalities for the transmitted

data, since path loss and multi-path fading affecting UW-A channels lead to high bit error

rates (on the order of10−2−10−5 [86][81]). While Automatic Repeat Request (ARQ) tech-

niques appear not to be suitable for the underwater environment because they incur a high

latency, additional energy cost, and signaling overhead due to retransmissions, Forward Er-

ror Correction (FEC) techniques can be effectively employed in such an environment. The

objective of these techniques is to protect data by introducing redundant bits in the trans-

mission so that the receiver can correct detected bit errors. In this way, retransmissions

are not necessary although both the transmitter and the receiver incur additional process-

ing power drain for encoding and decoding, respectively. There is a trade-off between the

robustness of the adopted FEC technique, which depends on the amount of redundant bits

30

injected in the channel, and the channel efficiency. A possible solution to maximize the

underwater channel efficiency in such a way as to effectively exploit its valuable available

bandwidth would be to dynamically choose the optimal amount of redundant bits according

to measurements of the underwater channel.


To enable data link layer solutions specifically tailored for underwater acoustic sensor net-

works, the following open research issues need to be addressed:

• In case CDMA is adopted, which we strongly advocate, it is necessary to design ac-

cess codes with high auto-correlation and low cross-correlation properties to achieve

minimum interference among users. This needs to be achieved even when the trans-

mitting and receiving nodes are not synchronized.

• Research on optimal data packet length is needed to maximize the network efficiency.

• It is necessary to design low-complexity encoders and decoders to limit the process-

ing power required to implement FEC functionalities. Researchers should evaluate

the feasibility and the energy-efficiency of non-convolutional error control coding

schemes.

• Distributed protocols should be devised to reduce the activity of a device when its

battery is depleting without compromising network operation.

2.7 Network Layer

Thenetwork layeris in charge of determining the path between a source (the sensor that

samples a physical phenomenon) and a destination node (usually the surface station). In

general, while many impairments of the underwater acoustic channel are adequately ad-

dressed at the physical and data link layers, some other characteristics, such as the ex-

tremely long propagation delays, are better addressed at the network layer.

31

In the last few years there has been an intensive study in routing protocols for ad hoc

wireless networks [3] and sensor networks [5]. However, because of the different nature

of the underwater environment and applications, there are several drawbacks with respect

to the suitability of the existing solutions for underwater acoustic networks. The existing

routing protocols are usually divided into three categories, namelyproactive, reactiveand

geographicalrouting protocols:

• Proactive protocols (e.g., DSDV [64], OLSR [40]). These protocols attempt to

minimize the message latency induced by route discovery, by maintaining up-to-date

routing information at all times from each node to every other node. This is obtained

by broadcasting control packets that contain routing table information (e.g., distance

vectors). These protocols provoke a large signaling overhead to establish routes for

the first time and each time the network topology is modified because of mobility

or node failures, since updated topology information has to be propagated to all the

nodes in the network. This way, each node is able to establish a path to any other node

in the network, which may not be needed in UW-ASNs. For this reason, proactive

protocols are not suitable for underwater networks.

• Reactive protocols(e.g., AODV [63], DSR [42]). A node initiates a route discovery

process only when a route to a destination is required. Once a route has been estab-

lished, it is maintained by a route maintenance procedure until it is no longer desired.

These protocols are more suitable for dynamic environments but incur a higher la-

tency and still require source-initiated flooding of control packets to establish paths.

Thus, both proactive and reactive protocols incur excessive signaling overhead be-

cause of their extensive reliance on flooding. Reactive protocols are deemed to be

unsuitable for UW-ASNs as they also cause a high latency in the establishment of

paths, which may be even amplified underwater by the slow propagation of acoustic

signals. Furthermore, links are likely to be asymmetrical, due to bottom characteris-

tics and variability in sound speed channel. Hence, protocols that rely on symmetrical

32

links, such as most of the reactive protocols, are unsuited for the underwater envi-

ronment. Moreover, the topology of UW-ASNs is unlikely to vary dynamically on a

short time scale.

• Geographical Routing Protocols(e.g., GFG [11], PTKF [57]). These protocols

establish source-destination paths by leveraging localization information, i.e., each

node selects its next hop based on the position of its neighbors and of the destination

node. Although these techniques are very promising, it is still not clear how accurate

localization information can be obtained in the underwater environment with limited

energy expenditure. In fact, fine-grained localization usually requires strict synchro-

nization among nodes, that is difficult to achieve underwater due to the variable prop-

agation delay. In addition Global Positioning System (GPS) receivers, which may be

used in terrestrial systems to accurately estimate the geographical location of sensor

nodes, do not work properly underwater. In fact, GPS uses waves in the1.5 GHz

band and those waves do not propagate in water.

Some recent papers propose network layer protocols specifically tailored for underwa-

ter acoustic networks. In [92], a routing protocol is proposed that autonomously establishes

the underwater network topology, controls network resources and establishes network flows

which relies on a centralized network manager running on the surface station. The man-

ager implements routing agents that periodically probe the nodes to estimate the channel

characteristics. This information is exploited by the manager to establish efficient data de-

livery paths in a centralized fashion, which allows avoiding congestion and providing some

form of Quality of Service (QoS) guarantee. The performance evaluation of the proposed

mechanisms has not been thoroughly studied yet.

In [81] it is shown with simple acoustic propagation models [13] that multi-hop routing

saves energy in underwater networks with respect to single hop communications, especially

with distances of the order of some kilometers. Based on this, a simple ad hoc underwater

network is designed and simulated where routes are established by a central manager based

33

on neighborhood information gathered by all nodes by means of poll packets.

In general, while most developed protocols for terrestrial ad hoc networks, mostly due

to scalability and mobility concerns, are based onpacket switching, i.e., the routing function

is performed separately for each single packet and paths are dynamically established,vir-

tual circuit routing techniques can be considered in UW-ASNs. In these techniques, paths

are establisheda priori between each source and sink, and each packet follows the same

path. This may require some form of centralized coordination and implies a less flexible ar-

chitecture, but allows exploiting powerful optimization tools on a centralized manager (e.g.,

the surface station) to achieve optimal performance at the network layer (minimum delay

paths, energy efficient paths, etc.), with minimum communication signaling overhead.

Furthermore, routing schemes that account for the 3D underwater environment need to

be devised. Especially, in the 3D case the effect of currents should be taken into account,

since the intensity and the direction of currents are dependent on the depth of the sensor

node. Thus, underwater currents can modify the relative position of sensor devices and also

cause connectivity holes, especially when ocean-column monitoring is performed in deep

waters.


There exist many open research issues for the development of efficient routing solutions

for underwater acoustic sensor networks, as outlined below:

• There is a need to develop algorithms to provide strict or loose latency bounds for

time critical applications. To this respect, it should be considered that while the delay

for an acoustic signal to propagate from one node to another mainly depends on the

distance of the two nodes, the delay variance also depends on the nature of the link,

i.e., the delay variance in horizontal acoustic links is generally larger than in vertical

links because of multipaths [82].

• For delay tolerant applications, there is a need to develop mechanisms to handle loss

34

of connectivity without provoking immediate retransmissions. Strict integration with

transport and data link layer mechanisms may be advantageous to this end.

• It is necessary to devise routing algorithms that are robust with respect to the in-

termittent connectivity of acoustic channels. The quality of acoustic links is highly

unpredictable, since it mainly depends on fading and multi-path, which are hard phe-

nomena to model.

• Accurate modeling is needed to better understand the dynamics of data transmission

at the network layer. Moreover, credible simulation models and tools need to be

developed.

• Algorithms and protocols need to be developed that detect and deal with disconnec-

tions caused by failures, unforeseen mobility of nodes, or battery depletion. These

solutions should be local so as to avoid communication with the surface station and

global reconfiguration of the network, and should minimize the signaling overhead.

• Local route optimization algorithms are needed to react to consistent variations in

the metrics describing the energy efficiency of the underwater channel. These vari-

ations can be caused by increased bit error rates due to acoustic noise and relative

displacement of communicating nodes.

• Mechanisms are needed to integrate AUVs in underwater networks and to enable

communication between sensors and AUVs. In particular, all the information avail-

able to sophisticated AUVs (trajectory, localization) could be exploited to minimize

the signaling needed for reconfigurations.

• In case of geographical routing protocols, it is necessary to devise efficient underwa-

ter positioning systems.

35

2.8 Transport Layer

A transport layer protocol is needed in UW-ASNs to achievereliable transportof event

features, and to performflow controlandcongestion control. Most existing TCP implemen-

tations are unsuited for the underwater environment since the flow control functionality is

based on a window-based mechanism that relies on an accurate estimate of the Round Trip

Time (RTT). The long RTT, which characterizes the underwater environment, would affect

the throughput of most TCP implementations. Furthermore, the variability of the underwa-

ter RTT would make it hard to effectively set the timeout of the window-based mechanism,

which most current TCP implementations rely on.

Existing rate-based transport protocols seem to be unsuited for this challenging envi-

ronment as well, since they rely on feedback control messages sent back by the destination

to dynamically adapt the transmission rate. The long and variable RTT can thus cause in-

stability in the feedback control. For these reasons, it is necessary to devise new strategies

to achieve flow control and reliability in UW-ASNs.

A transport layer protocol designed for the underwater environment, Segmented Data

Reliable Transport (SDRT), has been recently proposed in [93]. SDRT addresses the chal-

lenges of underwater sensor networks for reliable data transport, i.e., large propagation

delays, low bandwidth, energy efficiency, high error probabilities, and highly dynamic net-

work topologies. The basic idea of SDRT is to use Tornado codes to recover errored packets

to reduce retransmissions. The data packets are transmitted block-by-block and each block

is forwarded hop-by-hop. SDRT keeps sending packets inside a block before it gets back a

positive feedback and thus wastes energy. To reduce such energy consumption, a window

control mechanism is adopted. SDRT transmits the packets within the window quickly,

and the remaining packets at a lower rate. A mathematical model is developed to estimate

the window size and the FEC block size. The performance of SDRT is also illustrated by

simulations.

Encoding and decoding using Tornado codes are computation-intensive operations even

36

though Tornado codes use only XOR operations. This leads to increased energy consump-

tion. In SDRT, there is also no mechanism to guarantee the end-to-end reliability as an

hop-by-hop transfer mode is used. Each node along the path must first decode the FEC

block and then encode it again to transmit it to the next hop. Again, the total computa-

tion overhead will be too high for the network. Similarly, for hop-by-hop operations, each

sensor must keep calculating the mean values of window and the FEC block sizes, which

can cause a high computational overhead and accordingly higher energy consumption at

each sensor. The overhead due to redundant packets will also be high because of high error

probabilities. This overhead is dependent on the accuracy in estimating the window size. If

the window size is too large, more packets are sent than necessary. In addition, SDRT does

not address one of the fundamental challenges for UW-ASN, i.e., shadow zones, and relies

on an in-sequence packet forwarding scheme. While this may be enough for some appli-

cations, for time-critical data sensors may need to forward packets continuously even in

case of holes in the sequence with an out-of-sequence packet delivery mechanism. SDRT

is a first attempt to propose a transport protocol for UW-ASN and addresses some of the

aforementioned design principles. However, it is still an evolving work and needs further

improvements, as it creates redundant transmissions and is computation-intensive.

A complete transport layer solution for the underwater environment should be based on

the following design principles:

• Shadow zones.Although correct handling of shadow zones requires assistance from

the routing layer, a transport protocol should consider these cases.

• Minimum energy consumption.A transport protocol should be explicitly designed to

minimize the energy consumption.

• Rate-based transmission of packets.A transport protocol should be based on rate-

based transmission of data units as it allows nodes flexible control over the rates.

• Out-of-sequence packet forwarding.Packets should be continuously forwarded to

37

accelerate the packet delivery process.

• Timely reaction to local congestion.A transport protocol should adapt to local condi-

tions immediately, to decrease the response time in case of congestion. Thus, rather

than sinks, intermediate nodes should be capable of determining and reacting to local

congestion.

• Cross-layer-interaction-based protocol operation.Losses of connectivity or partial

packet losses (i.e., bit or packet errors) should trigger the protocol to take appropriate

actions. Therefore, unlike in the layered communications paradigm, transport proto-

col operations and critical decisions should be supported by the available information

from lower layers.

• Reliability. A hop-by-hop reliability mechanism surfaces as a prevalent solution as it

provides energy efficient communication. However, there should also be mechanism

to guarantee the end-to-end reliability.

• SACK-based loss recovery.Many feedbacks with ACK mechanisms would throttle

down the utilization of the bandwidth-limited channel unnecessarily. Thus, the no-

tion of selective acknowledgment (SACK), which helps preserve energy, should be

considered for loss scenarios where it is not possible to perform error recovery at

lower layers only.

Open research issues for transport layer solutions are given below:

• New flow control strategies need to be devised to tackle the high delay and delay

variance of the control messages sent back by the receivers.

• New effective mechanisms tailored to the underwater acoustic channel need to be

developed to efficiently infer the cause of packet losses.

• New reliability-metric definitions need to be proposed, based on the event model and

on the underwater acoustic channel model.

38

• The effects of multiple concurrent events on the reliability and network performance

requirements must be studied.

• It is necessary to statistically model loss of connectivity events to devise mechanisms

to enable delay-insensitive applications.

• It is necessary to devise solutions to handle the effects of losses of connectivity

caused by shadow zones.

2.9 Application Layer

Although many application areas for underwater sensor networks can be outlined, to the

best of our knowledge the definition of an application layer protocol for UW-ASNs remains

largely unexplored.

The purpose of an application layer is multi-fold: i) provide a network management

protocol that makes hardware and software details of the lower layers transparent to man-

agement applications; ii) provide a language for querying the sensor network as a whole;

iii) assign tasks and advertise events and data.

No efforts in these areas have been made to date that address the specific needs of the

underwater acoustic environment. A deeper understanding of the application areas and

of the communication problems in underwater sensor networks is crucial to outline some

design principles on how to extend or reshape existing application layer protocols [8] for

terrestrial sensor networks.

Some of the latest developments in middleware may be studied and adapted to realize

a versatile application layer for underwater sensor networks. For example, the San Diego

Supercomputing Center Storage Resource Broker (SRB) [9] is a client-server middleware

that provides a uniform interface for connecting to heterogeneous data resources over a

network and accessing replicated data sets. SRB provides a way to access data sets and

resources based on their attributes and/or logical names rather than their names or physical

locations.

39

2.10 Experimental Implementations of Underwater Sensor Networks

A few experimental implementations of underwater acoustic sensor networks have been re-

ported in the last few years. In this section we describe two of them, one mainly concerned

with military application and the other with oceanographic observations.

The Front-Resolving Observational Network with Telemetry (FRONT) project at the

University of Connecticut relies on acoustic telemetry and ranging advances pursued by

the US Navy referred to as “telesonar” technology [20]. The Seaweb network for FRONT

Oceanographic Sensors involves telesonar modems deployed in conjunction with three

types of nodes, namelysensors, gateways, andrepeaters. Sensors are oceanographic instru-

ments connected serially to an acoustic modem. Gateways are surface buoys that relay data

from the subsurface network to the shore. Repeaters are acoustic modems that relay data

packets. In the various Seaweb/FRONT experiments,20 sensors and repeaters have been

deployed in shallow water (20 to 60 meter deep). By means of long range ocean bottom

active sensors, Acoustic Correlation Current Profilers (ACCP), sampling of the 3D water

column is achieved with a 2D network architecture. The network enables sensor-to-shore

data delivery and shore-to-sensor remote control.

Researchers from different fields gathered at the Monterey Bay Aquarium Research

Institute (MBARI) in August2003 for a month-long experiment to quantify gains in pre-

dictive skills for principal circulation trajectories, i.e., to study upwelling of cold, nutrient-

rich water in the Monterey Bay. Autonomous vehicle paths (AUVs, gliders, etc.), as well

as other ships, vessels and platforms, enabled unexampled observational capabilities. Ex-

tensive data are reported that show the variation of the characteristics of the circulation of

water during the various days of the experiment.

The work in [20] describes on-the-field experience with networked acoustic modems.

The setup of several real-time monitoring experiments of ocean currents performed in front

of Block Island, RI, is outlined. However, the paper does not provide details on the imple-

mented networking protocols.

40

CHAPTER III

DEPLOYMENT ANALYSIS FOR UNDERWATER ACOUSTIC

SENSOR NETWORKS

3.1 Preliminaries

In this chapter, we consider two communication architectures for UW-ASNs, i.e., thetwo-

dimensional architecture, where sensors are anchored to the bottom of the ocean, and the

three-dimensional architecture, where sensors float at different ocean depths covering the

entire monitored volume region. While the former is designed for networks whose ob-

jective is to monitor the ocean bottom, the latter is more suitable to detect and observe

phenomena that cannot be adequately observed by means of ocean bottom sensor nodes.

We propose different deployment strategies, and provide a mathematical analysis to study

deployment issues concerning both architectures, with the objectives below:

• Determine the minimum number of sensors needed to be deployed to achieve the

target sensing and communication coverage dictated by the application;

• Provide guidelines on how to choose the optimal deployment surface area, given a

target region;

• Study the robustness of the sensor network to node failures, and provide an esti-

mate of the number of redundant sensors to be deployed to compensate for possible

failures.

The remainder of this chapter is organized as follows. In Section 3.2, we review related

literature. In Section 3.3, we briefly describe the two-dimensional and three-dimensional

architectures for UW-ASNs, and discuss the relevant deployment challenges. In Section

41

3.4, we derive geometric properties of the triangular-grid deployment, evaluate the trajec-

tory of a sinking device under the presence of ocean currents, compute the deployment

surface area to deploy sensors when a 2D bottom target area needs to be covered, and pro-

vide an estimate of the number of redundant sensors to compensate for possible failures.

In Section 3.5, we propose and compare through simulation experiments three deployment

strategies for 3D UW-ASNs.

3.2 Related Work

The problem of sensing and communication coverage for terrestrial sensor networks has

been addresses in several papers. However, to the best of our knowledge, this work is the

first to study deployment issues for underwater sensor networks. Many previous deploy-

ment solutions and theoretical bounds assuming spatio-temporal correlation, mobile sen-

sors, redeployment of nodes, and particular deployment grid structures may not be feasible

for the underwater environment.

In particular, in [78], methods for determining network connectivity and coverage given

a node-reliability model are discussed, and an estimate of the minimum required node-

reliability for meeting a system-reliability objective is provided. An interesting result is

that connectivity does not necessarily imply coverage. As the node-reliability decreases, in

fact, the sufficient condition for connectivity becomes weaker than the necessary condition

for coverage. Although [78] provides useful theoretical bounds and insight into the deploy-

ment of wireless terrestrial sensor networks, the analysis is limited to grid structures. In

[39], two coordination sleep algorithms are compared, a random and a coordinated sleep

scheme. It is shown that when the density of the network increases, the duty cycle of the

network can be decreased for a fixed coverage. Although [39] provides sound coverage

algorithms for terrestrial sensor networks, its results cannot be directly applied to the un-

derwater environment where the sensor density is much lower than in the terrestrial case,

and spatio-temporal correlation cannot often be assumed [7]. In [98], sensor coverage is

42

achieved by moving sensor nodes after an initial random deployment. However, [98] re-

quires either mobile sensor nodes or redeployment of nodes, which may not be feasible

for UW-ASNs. In [74], sensing and communication coverage in a three-dimensional en-

vironment are rigorously investigated. The diameter, minimum and maximum degree of

the reachability graph that describes the network are derived as a function of the com-

munication range, while different degrees of coverage (1-coverage and, more in general,

k-coverage) for the 3D environment are characterized as a function of the sensing range.

Interestingly, it is shown that the sensing ranger required for 1-coverage is greater than

the transmission ranget that guarantees network connectivity. Since in typical applications

t ≥ r, the network is guaranteed to be connected when 1-coverage is achieved. Although

these results were derived for terrestrial networks, they can also be applied in the under-

water environment. Thus, in this chapter, we will focus on the sensing coverage when

discussing deployment issues in 3D UW-ASNS, as in three-dimensional networks it im-

plicitly implies the communication coverage.

3.3 Communication Architectures

We consider two communication architectures for underwater sensor networks, i.e., atwo-

dimensionaland athree-dimensional architecture[7], and identify the relevant deployment

challenges. As in terrestrial sensor networks, in UW-ASNs it is necessary to providecom-

munication coverage, i.e., all sensors should be able to establish multi-hop paths to the sink,

andsensing coverage, i.e., the monitored area should be covered by the sensors. More for-

mally, thesensing ranger of a sensor is the radius of the sphere that models the region

monitored by the sensor (sensing sphere). A portionAη of the monitored regionA is said

to bek-coveredif every point inAη falls within the sensing sphere of at leastk sensors.

Thek-coverage ratioηk of a monitored regionA is the fraction of the volume/area that is

k-covered by a 3D/2D UW-ASN, respectively. In the following, we will consider the case

of k = 1 both for 2D and 3D networks to obtain simple1-coverageη1 of the region, since

43

underwater sensors may be expensive devices and spatio-temporal correlation may not be

assumed [7].

3.3.1 Two-dimensional UW-ASNs

A reference architecture for two-dimensional underwater sensor networks is shown in Fig.

1, where deployed sensor nodes are anchored to the bottom of the ocean. Underwater

sensors may be organized in a cluster-based architecture, and be interconnected to one

or moreunderwater gateways(uw-gateways) by means of wireless acoustic links. Uw-

gateways are network devices in charge of relaying data from the ocean bottom network

to a surface station. They are equipped with a long-rangevertical transceiver, which is

used to relay data to asurface station, and with ahorizontal transceiver, which is used

to communicate with the sensor nodes to send commands and configuration data, and to

collect monitored data. The surface station is equipped with an acoustic transceiver, which

may be able to handle multiple parallel communications with the uw-gateways, and with a

long-range radio transmitter and/or satellite transmitter, which is needed to communicate

with anonshore sinkand/or to asurface sink.

The main challenges that arise with such two-dimensional architecture are: i) determine

the minimum number of sensors and uw-gateways that need to be deployed to achieve the

target sensing and communication coverage, which are dictated by the application require-

ments; ii) provide guidelines on how to choose the optimal deployment surface area, given

a target bottom area; iii) study the topology robustness of the sensor network to node fail-

ures, and provide an estimate of the number of redundant sensor nodes to be deployed

to compensate for failures. In Section 3.4, we discuss in detail these issues and provide

solutions.

3.3.2 Three-dimensional UW-ASNs

Three-dimensional underwater networks are used to detect and observe phenomena that

cannot be adequately observed by means of ocean bottom uw-sensor nodes, i.e., to perform

44

cooperative sampling of the 3D ocean environment. In this architecture, sensors float at

different depths to observe a given phenomenon. One possible solution would be to attach

each sensor node to a surface buoy, by means of wires whose length can be regulated

to adjust the depth of each sensor node. However, although this solution enables easy

and quick deployment of the sensor network, multiple floating buoys may obstruct ships

navigating on the surface, or they can be easily detected and deactivated by enemies in

military settings. Furthermore, floating buoys are vulnerable to weather and tampering or

pilfering.

A different approach is to anchor winch-based sensor devices to the bottom of the

ocean, as depicted in Fig. 2. Each sensor is anchored to the ocean bottom and is equipped

with a floating buoy that can be inflated by a pump. The buoy pulls the sensor towards the

ocean surface. The depth of the sensor can then be regulated by adjusting the length of the

wire that connects the sensor to the anchor, by means of an electronically controlled engine

that resides on the sensor [7].

Many challenges arise with such architecture, which need to be solved to enable un-

derwater monitoring, including: i) sensors should collaboratively regulate their depth to

achieve 3Dsensing coverageof the ocean column, according to their sensing ranges; ii)

sensors should be able to relay information to the surface station via multi-hop paths, as

in 3D underwater networks there may be no notion of uw-gateway. Thus, network devices

should coordinate their depths in such a way as to guarantee that the network topology be

always connected, i.e., at least one path from every sensor to the surface station always

exists, and achievecommunication coverage. We discuss sensing and communication cov-

erage in 3D UW-ASNs in Section 3.5, and propose three deployment solutions.

3.4 Deployment in a 2D Environment

In this section, we provide a mathematical analysis of the graph properties of sensor devices

that are deployed on the surface of the ocean, sink, and reach the ocean bottom. To achieve

45

this, we study the trajectory of sinking devices (sensors and uw-gateways) when they are

deployed on the ocean surface with known initial conditions (position and velocity). This

allows us to capture both the case when sensor nodes arerandomly deployedon the ocean

surface, e.g., scattered from an airplane, or the case when sensors areaccurately positioned,

e.g., released from a vessel.

To address the deployment challenges presented in the previous section, in Section

3.4.1 we propose thetriangular-grid deployment, and derive useful geometric properties.

In Section 3.4.2, we study the dynamics of a sinking object and evaluate its trajectory un-

der the presence of ocean currents. In Section 3.4.3, we characterize the different sinking

behavior of sensors and uw-gateways, with the objective of describing their average hor-

izontal displacement and study the main communication properties of sensor clusters. In

Section 3.4.4, we derive the side margins that should be used to deploy sensors on the ocean

surface when a 2D target area needs to be covered on the ocean bottom under the presence

of currents. Finally, in Section 3.4.5, we derive an estimate of the number of redundant

sensors to be deployed to compensate for possible failures and provide the network with

robustness.

3.4.1 Triangular-grid Coverage Properties

In this section, we propose thetriangular-grid deployment, and derive useful geometric

properties. Let us consider the common case of sensors with same sensing ranger. The op-

timal deployment strategy to cover a two-dimensional rectangular area using the minimum

number of sensors is to center each sensor at the vertex of a grid of equilateral triangles, as

shown in Fig. 6. With this configuration, by adjusting the distanced among sensors, i.e.,

the side of the equilateral triangles, it is possible to achievefull coverage, i.e., η = 1. In

addition, this enables to optimally control the coverage ratioη, defined as the ratio between

the covered area and the target area. In particular, as it will be mathematically proven in the

following, whend =√

3r the coverage ratioη is equal to1, i.e., the uncovered areaABC

46

Figure 6: Triangular-grid deployment.Grid structure and side margins

Figure 7: Triangular-grid deployment.Uncovered area

47

depicted in Figs. 6 and 7 is zero, and the overlapping areas are minimized. This allows

to achieve the full coverage of a target area, but requires the highest number of sensors.

Conversely, as the distance among sensors increases, i.e., the number of deployed sensors

decreases, the coverage ratio decreases. Therefore, there is a trade-off between the number

of deployed sensors and the achievable sensing coverage. We are interested in finding the

minimum number of sensors that need to be deployed to guarantee a target sensing cov-

erageη∗, which is dictated by the application requirements. To this end, we present the

following theorem.

Theorem 1 In an equilateral grid the sensing coverageη(d, r), i.e., the ratio of the covered

area and the target area, is

η(d, r) = η

(d

r

)=

ADEF−AABC

ADEF= 1− AABC√

34

d2

dr∈ [0, 2]

3·πr2

6√3

4d2

= 2π√3· (d

r)−2 d

r∈ (2,∞),

(1)

where:

AABC =√

34

(d2−

√3r2 − 3

4d2

)2

− 3r2 arcsin BC2r

+34BC

√4r2 −BC

2, BC = d

2−

√3r2 − 3

4d2.

(2)

Proof With reference to Fig. 7, which represents a zoomed portion of Fig. 6,AE = r and

EH = d/2, wherer is the sensing range andd is the distance between sensors. Since the

triangleDEF is equilateral by construction,HO = (√

3/6)d. Consequently, sinceAH =√

r2 − d2/4, it holdsAO = HO − AH = (√

3/6)d −√

r2 − d2/4. As triangleDEF is

equilateral, triangleABC is equilateral too. SinceAO = (√

3/3)BC, thenBC = d/2 −√

3r2 − (3/4)d2. Therefore, the area of triangleABC isA4ABC = (

√3/4)BC

2. To be able

to express the sensing coverageη(d, r) as a function ofd andr, we need to compute the area

AABC of theuncovered regionABC among the circles with centers inD, E, andF , and

radiusr. This can be computed asAABC = A4ABC − 3 · ABTCK , whereABTCK coincides

with the difference of the areas of the circular sectorBTCF and the triangleBCF , i.e.,

ABTCK = ABTCF −A4BCF = r2 arcsin(BC/2r)− (BC/4)

√4r2 −BC

2. Consequently,

48

1 1.5 2 2.5

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

Ratio of sensor distance and sensing range (d/r)

Sen

sing

cov

erag

e

η(d)d/r=sqrt(3)d/r=2(η*,d/r*)=(0.95,1.95)

Figure 8: Triangular-grid deployment.Sensing coverage

AABC = (√

3/4)(d/2−

√3r2 − (3/4)d2

)2

−3r2 arcsin(BC/2r)+(3/4)BC

√4r2 −BC

2,

whereBC = d/2 −√

3r2 − (3/4)d2, which gives (1) in the non-trivial cased/r ∈ [0, 2].

As far as the cased/r ∈ (2,∞) is concerned, no overlapping areas are formed, and the

coverageη can be computed straightforward.

Corollary 1 In an equilateral grid the sensing coverage depends only on the ratio of the

inter-sensor distanced and the sensing ranger, and not on their absolute values, i.e.,

η(d, r) = η(d/r).

Let us note in (1) that, whend/r ≤ √3, it holdsA4

ABC = AABC = 0, which means

that in this case the highest possible coverage is achieved (η = 1). Moreover,AABC(d) is a

monotonically increasing function whend/r ranges in[√

3, 2], which makes the coverage

η(d, r) a monotonically decreasing function whend/r >√

3. Figure 8 reports the sensing

coverage as a decreasing function of the ratio ofd andr. For a target sensing coverage

η∗ = 0.95, it is shown that the optimal ratio isd∗/r = 1.95.

In order to compute the minimum number of sensors that need to be deployed to cover

a target area with sidesl andh using the proposed equilateral grid, we should first find the

49

optimal margins∆l and∆h from the center of the upper-left sensing circle, as shown in

Fig. 6. In particular, given the application-dependent target coverageη∗, from Fig. 8 we

compute the optimal ratiod∗/r. In order for the uncovered areas on the border of the target

area to have the same coverage ratioη∗, the margins should be selected as∆h = HO +

OT = (√

3/2)d∗−r, whereOT = OF −TF = (√

3/3)d−r, and∆l = 2OH cos(π/6) =

d∗/2. If we denote asN∗ the minimum number of sensors, we haveN∗ = N∗l · N∗

h ,

whereN∗l andN∗

h represent the minimum number of sensors deployed along sidesl andh,

respectively. Consequently, the following relations need to be satisfied,

2∆l + (N∗l − 1)d∗ ≥ l ⇒ N∗

l =⌈

l−d∗d∗ + 1

⌉

2∆h + (N∗h − 1)d∗ sin(π/6) ≥ h ⇒ N∗

h =⌈

2√

3h−6d∗+4√

3r3d∗ + 1

⌉.

Finally, the minimum number of sensorsN∗ required to cover a target area with sidesl and

h, under the constraints of providing a ratiod∗/r to satisfy the target coverage ratioη∗ is

N∗(l, h, d∗, r) =

⌈l − d∗

d∗+ 1

⌉·⌈

2√

3h− 6d∗ + 4√

3r

3d∗+ 1

⌉. (3)

In Figs. 9-11, (3) is plotted for three different target areas, i.e.,A1 = 100x100 m2, A2 =

300x200 m2, andA3 = 1000x1000 m2, and for several sensing rangesr in the interval

[10, 35] m.

3.4.2 Trajectory of a Sinking Object

In this section, we study the dynamics of a sinking object and evaluate its trajectory under

the presence of ocean currents. In particular, we first consider the ideal case in which the

velocity of the ocean current does not change with depth; then, we extend the model to

capture the more realistic case in which the velocity of the current depends on depth.

According to Newton’s first law of motion, the acceleration~a describing the sinking

in the water of an object with a densityρ and volumeV is determined by the following

vectorial motion law,

~FW + ~FB + ~FR + ~FC = ρV · ~a, (4)

where:

50

1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.50

5

10

15

20

25

30

35

40

45

50

Ratio of sensor distance and sensing range (d*/r)

Min

imum

no.

of s

enso

rs

r=10mr=15mr=20mr=25mr=30mr=35mArea: lxh= 100x100m2

Figure 9: Minimum number of sensors in triangular-grid deployment vs. sensor distanceover sensing range.A1 = 100x100 m2

1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.50

50

100

150

200

250

300


Min

imum

no.

of s

enso

rs



51

1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.50

500

1000

1500

2000

2500

3000

3500

4000


Min

imum

no.

of s

enso

rs



• ~FW = ρV · ~g is theweight force, which depends on the densityρ [Kg/m3] and volume

V [m3] of the sinking object, and on the terrestrial gravitational accelerationg = 9.81m/s2;

• ~FB = −ρwV · ~g is thebuoyant forcedue to the Archimede’s principle, which is equal to

the weight of the displaced fluid, whereρw = 1050 Kg/m3 represents the average density of

salty water;

• ~FR = −KρwµAR ·~v is thefluid resistance force, which is proportional through the constant

K = 0.2Nm2s/Kg [77] to the velocity~v [m/s] of the object, to its cross-sectionAR [m2],

and to a parameterµ accounting for the resistance caused by the object shape;

• ~FC = CσAC ·(~vc−~v) is theforce of the current, which is proportional through the constant

C = 721.7Ns/m3 [77] to the difference between the velocity of the ocean current~vc [m/s]

and the object velocity~v [m/s], to the cross-sectionAC [m2] of the object facing the current,

and to an object-dependent shape factorσ.

We project (4) onto the x-, y-, and z- axes, which are directed as shown in Fig. 12,

and we denote the dynamic position of the sinking object asP = (x, y, z), its velocity as

~v = (x, y, z), and its acceleration as~a = (x, y, z). We then consider the velocity of the

52

Figure 12: Trajectory of a sinking object

current~vc = (vxc , vy

c , vzc ), which, for the sake of clarity, is first assumed to be independent

on the ocean depth (we will then relax this assumption). Under the assumption that no

significant vertical movement of ocean water is observed, i.e., the considered area is neither

anupwellingnor adownwelling area, the current along the z-axes can be neglected (vzc ≈

0), and (4) leads to three scalar laws,

x : F xC = ρV x; y : F y

C = ρV y; z : F zW + F z

B + F zR = ρV z. (5)

Specifically, we obtain the following dynamic system equations,

x + CσAxy

ρVx = CσAxy

ρVvx

c

y + CσAxy

ρVy = CσAxy

ρVvy

c

z + KµρwAz

ρVz = g ρ−ρw

ρ,

(6)

whereAxy andAz represent the horizontal and vertical cross-sections, respectively. By

solving this dynamic system, with the initial conditions of the object on the surface at time

t0, i.e., its positionP(t0) = (x(t0), y(t0), 0) and velocity~v(t0) = (x(t0), y(t0), z(t0)), we

53

obtain the solution,

x(t) = x(t0) + vxc · (t− t0) + x(t0)−vx

c

CσAxy/ρV· [1− e−

CσAxy

ρV·(t−t0)]

y(t) = y(t0) + vyc · (t− t0) + y(t0)−vy

c

CσAxy/ρV· [1− e−

CσAxy

ρV·(t−t0)]

z(t) = vz∞ · (t− t0) + [z(t0)− vz

∞] · [1− e−KρwµAz

ρV·(t−t0)],

(7)

where we denoted asvz∞ = gV (ρ−ρw)

KρwµAz [m/s] theterminal velocityalongz, which is computed

by imposing in (5) the following force equilibrium,F zW + F z

B + F zR = 0, i.e., z = 0 in (6).

Let us now generalize this result by considering the more realistic case in which the

velocity of the ocean current depends on depth, i.e.,~vc = (vxc (z), vy

c (z), 0). There are two

types of marine currents each caused by a range of distinct drivers,non tidalocean currents,

such as the Gulf Stream, andtidal streams. The complex hydrodynamic system of currents

is powered by many forces, the crux being the playoff between the joint forces of solar

heating of tropical surface waters and the polar contributions of cold fresh water ice-melt

flooding into the ocean and the general cooling of the salty ocean water. While studying

the global current systems makes up the larger part of the science ofoceanography, in this

chapter we focus on the effect oflocal streamsin the monitored volume region. In partic-

ular, we consider an ocean volume with constant depthzH (flat bottom), andH different

ocean current layersh = 1, ..., H, of width ∆zh. We model the current on each plane xy

in a layerh to be a piecewise constant function with modulevhc and angular deviation from

the x-axesαhc , as depicted in Fig. 12. This allows us to model thethermohaline circulation

(also known as the ocean’s conveyor belt), i.e., deep ocean current, sometimes calledsub-

marine rivers, that flows with constant velocity and direction within certain depths, driven

by density and temperature gradients.

Given these assumptions, our objective is to calculate the horizontal displacement of a

sinking object on the x- and y-axes in each of the layers it sinks through. To accomplish

this, we recursively apply the solution (7) to the dynamic system (6) to each layer, using as

initial conditions of the object the final position and velocity computed in the previous layer.

If we denote the initial position of objectn as(x0n, y0

n, 0) and its velocity as(x0n, y

0n, z0

n),

54

given all its physical characteristics such as volumeVn, densityρn, cross-sectionsAxyn and

Azn, and horizontal and vertical shape factors,µn andσn, respectively, we can track the

position ofn while it sinks. Specifically, we have

xn(t) = x0n +

∑h−1i=1 ∆xi

n + vhc cos αh

c · (t− th−1n )+

+ xn(th−1n )−vh

c cos αhc

CσnAxyn /ρnVn

· [1− e−CσnA

xyn

ρnVn·(t−th−1

n )]

yn(t) = y0n +

∑h−1i=1 ∆yi

n + vhc sin αh

c · (t− th−1n )+

+ yn(th−1n )−vh

c sin αhc

CσnAxyn /ρnVn

· [1− e−CσnA

xyn

ρnVn·(t−th−1

n )]

th−1n ≤ t ≤ thn

zn(t) = min{vz∞n · (t− t0n)+

+[z0n − vz

∞n] · [1− e−KρwµnAz

nρnVn

·(t−t0n)]; zH},

(8)

wheret0n andthn are the instants objectn is released on the ocean surface and exits layerh,

respectively. More precisely,thn is the instant for which it holdszn(thn) = zh =∑h

i=1 ∆zi,

i.e., the depth of the object coincides with the sum of the width∆zi of each layeri the

object sank through, as shown in Fig. 12.

In (8), the total displacement on the x- and y-axes when the sinking object is inside layer

h is recursivelycomputed as the sum of the displacements in each of theh− 1 previously

crossed layersi = 1, ..., h− 1, plus the displacement in layerh itself. These displacements

are determined as partial solution of the dynamic system (6) in each layer, and have the

following structure,

∆xin = vi

c cos αic · (tin − ti−1

n )+

+ xn(th−1n )−vh

c cos αhc

CσnAxyn /ρnVn

· [1− e−CσnA

xyn

ρnVn·(tin−ti−1

n )]

∆yin = vi

c sin αic · (tin − ti−1

n )+

+ yn(th−1n )−vh

c sin αhc

CσnAxyn /ρnVn

· [1− e−CσnA

xyn

ρnVn·(tin−ti−1

n )].

(9)

Finally, to be able to determine the position of objectn from (8), we need to substitute

in (8) and (9) the x- and y-component of the velocity the object has when it enters layer

h = 1, ..., H, i.e.,(xn(th−1n ), yn(th−1

n )), which can be computed as exit velocity from layer

55

h− 1 by solving (6). We report these velocities in the following,

xn(th−1n ) = vh−1

c cos αh−1c +

+[xn(th−2n )− vh−1

c cos αh−1c ] · e−CσnA

xyn

ρnVn·(th−1

n −th−2n )

yn(th−1n ) = vh−1

c sin αh−1c +

+[yn(th−2n )− vh−1

c sin αh−1c ] · e−CσnA

xyn

ρnVn·(th−1

n −th−2n ),

(10)

which can be recursively computed given thatxn(t0n) andyn(t0n) are the known initial ve-

locities on the surface.

Equations (8), (9), and (10) allow us to track the dynamic position of objectn while it

sinks, given complete knowledge about the structure of the currents in the volume of inter-

est. In practice, however, we may only leverage some statistical information on the currents,

which can be used to estimate the final position of a deployed object. While this offers a

mathematical tool to study the dynamic of a sinking object, our ultimate objective is to

be able to infer the statistical sensing and communication properties of a two-dimensional

sensor network that reaches the ocean bottom, as will be discussed in the following section.

3.4.3 Communication Properties of 2D UW-ASNs

In this section, we characterize the different sinking behavior of sensors and uw-gateways,

with the objective of describing: i) the average horizontal displacement of sensors and uw-

gateways when different depths and current velocities are considered; ii) the main proper-

ties of the clusters that have an uw-gateway as cluster head, e.g., study the maximum and

average sensor-gateway distance when the number of deployed gateways varies; iii) the

average and standard deviation of number of sensors in each cluster.

Let us consider a set of sensorsS with cardinalityS = |S| characterized by the same

densityρS , volumeVS, cross-sectionsAxyS andAz

S , and shape factorsµS andσS , and a

set of uw-gatewaysG with G = |G|, in general with different values ofρG, VG, AxyG , Az

G,

µG, andσG. Given the matrices of the known initial positions of the deployed sensors

56

and uw-gateways,P0S = [P0

1| · · · |P0s | · · · |P0

S]T andP0G = [P0

1| · · · |P0g| · · · |P0

G]T , respec-

tively, whereP0s = [x0

s y0s 0]T ∀s ∈ S andP0

g = [x0g y0

g 0]T ∀g ∈ G are position column

vectors, and the matrices of their known initial velocities,v0S = [v0

1| · · · |v0s | · · · |v0

S]T and

v0G = [v0

1| · · · |v0g| · · · |v0

G]T , wherev0s = [x0

s y0s z0

s ]T ∀s ∈ S andv0

g = [x0g y0

g z0g ]

T ∀g ∈ Gare velocity column vectors, the final positions on the ocean bottom of the sensors and

uw-gateways,PfS andPf

G, respectively, can be derived using (8), (9), and (10) when all de-

ployed devices have reached the bottom, i.e., whent = tf ≥ max{maxs∈S tHs ; maxg∈G tHg }.Specifically,

PfS = P0

S + ∆PS(v0S), Pf

G = P0G + ∆PG(v0

G) (11)

where∆PS(v0S) and∆PG(v0

G) are matrices accounting for the total displacements accu-

mulated while the sensors and uw-gateways, respectively, were sinking through the ocean

current layers, i.e.,

∆PS =

.∑H

h=1 ∆xhs .

.∑H

h=1 ∆yhs .

. zH .

T

,∆PG =

.∑H

h=1 ∆xhg .

.∑H

h=1 ∆yhg .

. zH .

T

. (12)

In (12), each element can be computed as in (9). Note that the dependence on the initial

velocity in (12) has been omitted for the sake of notation simplicity.

In Fig. 13, we show the expected horizontal displacement∆d =√

∆x2 + ∆y2 of

sensors and uw-gateways when different depths and current velocities are considered. In

particular, we considerρs = 2000 kg/m3, ρg = 2500 kg/m3, Vs = 0.5 · 10−3 m3, and

Vg = 10−3 m3 to account for the common physical characteristics of underwater sen-

sor nodes and uw-gateways, which reflect into different sinking properties, as formal-

ized in (11). Note that gateways accumulate smaller displacements than sensors since

their sinking times are shorter. In Fig. 14, we depict the maximum and average sensor-

gateway distance when the number of deployed gateways increases. In particular, we

consider three deployment volumes (V1 = 100x100x50 m3, V2 = 300x200x100 m3, and

V3 = 1000x1000x500 m3) and a one-layer current scenario (H = 1) with vmaxc = 1 m/s.

57

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

10

20

30

40

50

60

70

80

Velocity of current [m/s]

Ave

rage

dis

plac

emen

t [m

]

Sensor @depth3=500m

Uw−gateway @depth3=500m

Sensor @depth2=100m


Sensor @depth1=50m


Figure 13: Average horizontal displacement of sensors and uw-gateways vs. current ve-locity (for three different depths)

According to the specific sensor transmission ranget, Fig. 14 allows setting the minimum

number of uw-gateways that need to be deployed. In Fig. 15, we present the normalized av-

erage and standard deviation of number of sensors per uw-gateway when two deployment

strategies are considered, therandomand thegrid deployment. Interestingly, while the av-

erage number of sensors does not depend on the deployment strategy, the sensor dispersion

is much lower in a grid structure, independently on the number of gateways deployed. This

is a general result that does not depend on the considered scenario.

3.4.4 Deployment Surface Area: Side Margins

In this section, we compute the deployment surface area where sensors should be deployed,

when a 2D target area needs to be covered on the bottom of the ocean. As described in Sec-

tions 3.4.2 and 3.4.3, ocean currents may significantly modify the sinking trajectories of

sensors and uw-gateways. Therefore, the surface deployment should take into account the

effect of the currents, to position as many deployed sensors inside the target area as possi-

ble. To achieve this, in the following we consider aworst-case scenariowhere the effect of

currents, in terms of sensor displacements, is captured. The objective is to dimension the

58

5 10 15 20 25 300

100

200

300

400

500

600

No. of deployed uw−gateways

Max

imum

and

ave

rage

sen

sor

<−

> u

w−

gate

way

dis

tanc

e [m

]

Dmax

@V3=1000x1000x500m3

Dav

@V3=1000x1000x500m3

Dmax

@V2=300x200x100m3

Dav

@V2=300x200x100m3

Dmax

@V1=100x100x50m3

Dav

@V1=100x100x50m3

Figure 14: Maximum and average sensor-gateway distance vs. number of deployed gate-ways (in three different volumes, and withvmax

c = 1 m/s)

5 10 15 20 25 300

100

200

300

400

500

600

No. of deployed uw−gateways

Max

imum

and

ave

rage

sen

sor

<−

> u

w−

gate

way

dis

tanc

e [m

]

Dmax

@V3=1000x1000x500m3

Dav

@V3=1000x1000x500m3

Dmax

@V2=300x200x100m3

Dav

@V2=300x200x100m3

Dmax

@V1=100x100x50m3

Dav

@V1=100x100x50m3

Figure 15: Normalized average and standard deviation of number of sensors per uw-gateway vs. number of deployed gateways (for grid and random deployment strategies,in three different volumes, and withvmax

c = 1 m/s)

59

deployment surface area, i.e., to asses propersurface side margins.

With reference to Fig. 16, we consider a bottom target area with sidesl andh, and

analyze the two cases ofunknown current direction(a), where we denote as∆dmax =√

∆x2max + ∆y2

max the maximum horizontal displacement a sinking sensor can experience,

i.e., how far in the horizontal plane xy a sensor can drift (see Fig. 12), andknown current

direction(b), where we denote as∆dmax the same metric used in the previous case and as

∆αmax the maximum angular deviation of the current from its known directionβ, which

is the angle the direction of the current forms with sideh of the target area, as depicted in

Fig. 16(b). Note that, without loss in generality, it always holds thatβ ∈ [0, π/2). More

specifically, the dottedcircular sector in Fig. 16(b), characterized by radius∆dmax and

angle2∆αmax, represents the region of the ocean bottom that may be reached by a sensor

that is deployed on the ocean surface exactly on the vertex of the circular sector itself. This

region represents the statistical uncertainty in the final anchor position of a sensor caused

by drifting due to ocean currents during the sinking.

As far as the side margins in the unknown current direction case are concerned, from

geometric properties of Fig. 16(a) it holds,

l∗ = l + 2∆dmax

h∗ = h + 2∆dmax,(13)

while for the known current direction case (Fig. 16(b)) it holds,

l∗ = l + ∆dmax · {max [0; sin(β −∆αmax)] + sin(β + ∆αmax)}h∗ = h + ∆dmax · {max [cos(β −∆αmax); cos(β + ∆αmax)]}.

(14)

In (13) and (14), the worst-case maximum displacement and maximum angular deviation a

sensor can experience are,

∆dmax =zH

H · vz∞·

H∑

h=1

vh,maxc (15)

∆αmax = arctan

∑Hh=1 vh,max

c · sin αh,maxc∑H

h=1 vh,maxc · cos αh,max

c

, (16)

60

Figure 16: Deployment surface area for unknown (a) and known (b) current directionβ,given a bottom target arealxh

wherezH is the ocean depth,H is the number of ocean current layers,vz∞ is the terminal

velocity (see Section 3.4.2), andvh,maxc andαh,max

c are the maximum current velocity and

angular deviation in layerh, respectively. The mathematical derivation of (14), (15), and

(16) is omitted for lack of space. Interestingly, given the same target area, the side surface

margins in the unknown current direction case (13) are larger than those computed if some

information about the current direction can be leveraged (14). This is also shown in Fig. 16,

where the surface areas (outside solid rectangles) in the two cases are noticeably different,

while the target area (inside dotted rectangle) is the same.

3.4.5 Reliability Margin

In this section, we provide an estimate of the number of redundant sensors required to

endow the network with robustness to node failures, which in the underwater may be caused

by fouling and corrosion. In particular, we study the required topology redundancy to

61

Table 3: Redundant sensors∆N∗ to compensate for failures

Obs. Time T [days] 30 60 90 120 150 180Γ∗1 = 0.90 2 4 5 7 8 9Γ∗2 = 0.95 3 5 6 7 9 10Γ∗3 = 0.99 4 6 8 9 11 12

statistically compensate for node failures within a predetermined observation period, i.e.,

the length of the monitoring mission. If we assume node failures to be independent and

occurring according to a Poisson distribution, the minimum number of redundant sensors

∆N∗ to be deployed to compensate for Poissonian failures is,

∆N∗∑n=0

(λT )n · e−λT

n!≥ Γ∗, (17)

whereλ [day−1] represents the sensor failure rate,T [day] the observation time,n the num-

ber of sensors that experience a failure within the observation time, andΓ∗ the target suc-

cess probability, i.e., the probability that no more than∆N∗ failures be experienced during

the observation time. Table 3 reports the number of redundant sensors that need to be

deployed to compensate for Poisson sensor failures occurring during several observation

times under three different success probabilities, whenλ = 1/(365/12) day−1, i.e., in av-

erage a sensor experiences one failure every month.

3.5 Deployment in a 3D Environment

In this section, we propose three deployment strategies for three-dimensional UW-ASNs

to obtain a target1-coverageη∗1 = η∗ of the 3D region, i.e., the3D-random, thebottom-

random, and thebottom-gridstrategies. As previously discussed, it is shown in [74] that

the sensing ranger required for 1-coverage is greater than the transmission ranget that

guarantees network connectivity. Since in typical applicationst ≥ r, the network is guar-

anteed to be connected when 1-coverage is guaranteed. Thus, in the following we focus on

the sensing coverage. In all these deployment strategies, winch-based sensor devices are

62

anchored to the bottom of the ocean in such a way that they cannot drift with currents. Sen-

sor devices are equipped with a floating buoy that can be inflated by a pump by means of an

electronically controlled engine that resides on the sensor. This way, they can adjust their

depth and float at different depths in order to observe a given phenomenon, as described in

Section 3.3.2. In all the proposed deployment strategies, described hereafter, sensors are

assumed to know their final positions by exploiting localization techniques.

3D-random. This is the simplest deployment strategy, and does not require any form

of coordination from the surface station. Sensors are randomly deployed on the bottom of

the 3D volume, where they are anchored. Then, each sensor randomly chooses its depth,

and, by adjusting the length of the wire that connects it to the anchor, it floats to the selected

depth. Finally, each sensor informs the surface station about its final position.

Bottom-random. As in the previous strategy, sensors are randomly deployed on the

bottom, where they are anchored. Differently from the 3D-random scheme, the surface

station is informed about their position on the bottom. Then, the surface station calculates

the depth for each sensor in order to achieve the target 1-coverage ratioη∗. Finally, each

sensor is assigned its target depth and floats to the desired position.

Bottom-grid. This deployment strategy needs to be assisted by one or multiple AUVs,

which deploy the underwater sensors to predefined target locations to obtain a grid deploy-

ment on the bottom of the ocean. Each sensor is also assigned a desired depth by the AUV

and accordingly floats to achieve the target coverage ratioη∗.

Algorithm 1 reports the pseudo code of the procedure run on the surface station to find

the optimal depths of the sensor nodes, for the bottom-random and bottom-grid strategies.

The positions of the sensor nodes are represented by a matrixP = [P1 |Pi |PN−1]T , where

Pi represents the 3D coordinates of theith sensor. TheN th node (alsoN for simplicity)

represents the sink, which is located on the surface of the ocean. For example,Pi(3)

represents the z coordinate of theith sensor. We refer to a discrete set of values, equally

spaced with stepstepz between0 (surface) andzmax (ocean bottom), for the depth of the

63

sensor nodes. The functionη(P, A, r) estimates the coverage ratioη given the positions

of the sensorsP, the target volumeA, and their sensing ranger.

Algorithm 1 3D Coverage Optimizationwhile (h ≤ max steps andη < η∗) do

for (i = 1; i < N ; i++) dofor (j = 0; j ≤ zmax/stepz; j++) do

zold = Pi(3)Pi(3) = j ∗ step zηnew = η(P, A, r)if (ηnew > η) then

η = ηnew

elsePi(3) = zold

end ifend for

end forh++

end while

In the following we calculate the minimum number of sensors needed to achieve a de-

sired target 1-coverage ratioη∗ for the proposed deployment strategies. As shown in Figs.

17-19, given a fixed number of sensors we achieve a better coverage ratio with increas-

ing complexity of the deployment strategy. In fact, the coverage ratio obtained with the

bottom-grid strategy is greater than the coverage ratio obtained with the bottom-random

strategy, which is in turn greater than the coverage ratio of the 3D-random strategy. More-

over, given a target coverage ratio, the minimum number of sensors needed to achieve the

desired coverage ratio decreases with the complexity of the deployment strategy. Figure

20 shows a comparison between the minimum normalized sensing range that guarantees

coverage ratios of 1 and 0.9 with the bottom-random strategy and the theoretical bound

on the minimum normalized sensing range derived in [74], where the authors investi-

gate sensing and communication coverage in a 3D environment. According to Theorem

4 in [74], the 3D volume is guaranteed to beasymptotically almost surely1-covered iff

43π n

Vr3 = ln n + ln ln n + ω(n), with 1 << ω(n) << ln ln n, whereV is the volume

64

0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 0.220

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Normalized sensing range

Fra

ctio

n of

3D

spa

ce c

over

ed

No. sensors=25No. sensors=36No. sensors=49No. sensors=64No. sensors=81No. sensors=100

Figure 17: Three-dimensional scenario.3D coverage with a 3D random deployment

0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 0.220

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1


Fra

ctio

n of

3D

spa

ce c

over

ed


Figure 18: Three-dimensional scenario.Optimized 3D coverage with a 2D bottom-random deployment

65

0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 0.220

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1


Fra

ctio

n of

3D

spa

ce c

over

ed


Figure 19: Three-dimensional scenario.Optimized 3D coverage with a 2D bottom-griddeployment

0 10 20 30 40 50 60 70 80 90 1000.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Number of sensors

Sen

sing

ran

ge

Minimum sensing range (coverage ratio=0.9)Minimum sensing range (coverage ratio=1)Sensing range bound

Figure 20: Theoretical and experimental sensing range

66

of the region to be covered,n the number of deployed sensors, andr their sensing range.

Hence, to draw Fig. 20 we setω(n) = 1+ln ln n2

. This shows that the bottom-random de-

ployment strategy very closely approximates the theoretically predicted bound, i.e., the

minimum sensing range that guarantees 1-coverage with probability 1 is almost the same

as that predicted by the model in [74].

67

CHAPTER IV

DISTRIBUTED ROUTING ALGORITHMS FOR UNDERWATER

ACOUSTIC SENSOR NETWORKS

4.1 Preliminaries

Many researchers are currently engaged in developing networking solutions for terrestrial

wireless ad hoc and sensor networks. Although there exist many recently developed net-

work protocols for wireless sensor networks, the unique characteristics of the underwater

acoustic communication channel, such as limited bandwidth capacity and high propagation

delays [70], require new efficient and reliable data communication protocols. Major chal-

lenges in the design of UW-ASNs are: i) the propagation delay is five orders of magnitude

higher than in radio frequency (RF) terrestrial channels, and variable; ii) the underwater

acoustic channel is severely impaired, especially because of multipath and fading; iii) the

available bandwidth is limited; iv) high bit error rates and temporary losses of connectivity

(shadow zones) can be experienced; v) underwater sensors are prone to failures because of

fouling and corrosion; vi) battery power is limited and usually batteries cannot be easily

recharged, also because solar energy cannot be exploited.

Most impairments of the underwater acoustic channel are adequately addressed at the

physical layer, by designing receivers able to deal with high bit error rates, fading, and the

inter-symbol interference (ISI) caused by multipath. Conversely, characteristics such as the

extremely long and variable propagation delays are better addressed at higher layers. For

example, the delay variance in horizontal acoustic links is generally larger than in vertical

links due to multipath [82]. In fact, the quality of acoustic links is highly unpredictable,

since it mainly depends on fading and multipath, which are not easily modeled phenomena.

68

Moreover, as in terrestrial sensor networks, energy conservation is one of the major con-

cerns, since batteries cannot be easily recharged or replaced. Finally, the bandwidth of the

underwater links is severely limited, and, differently from the terrestrial case, dependent on

the link distance [87]. Hence, routing protocols designed for underwater acoustic networks

must be extremely bandwidth and energy efficient.

In this chapter, we propose two geographical routing algorithms for the 3D underwater

environment that are designed to distributively meet the requirements of delay-insensitive

and delay-sensitive underwater sensor network applications. The proposed distributed rout-

ing solutions are tailored for the characteristics of the underwater environment, e.g., they

take explicitly into account the very high propagation delay, which may vary in horizon-

tal and vertical links, the different components of the transmission loss, the impairment

of the physical channel, the extremely limited bandwidth, the high bit error rate, and the

limited battery energy. These characteristics lead to very low efficiencies of the underwater

acoustic channel when a common random access technique is adopted to transmit a data

packet.

Conversely, our routing solutions allow achieving two apparently conflicting objectives,

i.e., increasing the efficiency of the acoustic channel by transmitting atrain of short packets

back-to-back; and limiting the packet error rate by keeping the length of the transmitted

packets short. The packet-train concept is exploited in the routing algorithms proposed

in this chapter. The algorithms are distributed routing solutions for delay-insensitive and

delay-sensitive applications, and allow each node tojointly select its best next hop, the

optimal transmitted power, and the forward error correction (FEC) rate for each packet,

with the objective of minimizing the energy consumption, while taking the condition of the

underwater channel and the application requirements into account.

The first routing algorithm deals with delay-insensitive applications, and sets the op-

timal combination of transmitting power and FEC strength in such a way as to exploit

those links that can guarantee a low packet error rate to maximize the probability that a

69

packet is correctly decoded at the receiver, thus minimizing the number of required packet

retransmissions and the overall energy required for successful transmissions.

The second routing algorithm is designed for delay-sensitive applications. The ob-

jective is to minimize the energy consumption, while statistically limiting the end-to-end

packet delay and packet error rate. To accomplish this, the algorithm estimates at each hop

the time to reach the sink and leverages statistical properties of underwater links. As in

the previous delay-insensitive routing solution, each node jointly selects its best next hop,

the transmitted power, and the forward error correction rate for each packet. However, dif-

ferently from the first routing algorithm, in order to meet the delay-sensitive application

requirements, next hops are selected by also considering maximum per-packet allowed de-

lay. In addition, unacknowledged packets are not retransmitted to limit the delay.

In both routing algorithms, the emphasis on energy consumption is justified by the need

for extended lifetime deployments of underwater sensor networks. While survivability is

another fundamental aspect of sensor networks, this has been dealt with in [65], where

a two-phase resilient routing algorithm for long-term applications in UW-ASNs was pro-

posed.

In addition, we propose an optimization problem to set the packet size for underwater

communications when a particular forward error correction scheme is adopted, given the

3D volume of water that the application needs to monitor, the density of the sensor network,

and the application requirements.

The remainder of this chapter is organized as follows. In Section 4.2, we discuss the

suitability of the existing ad hoc and sensor routing solutions for the underwater environ-

ment, and motivate the use of geographical routing in this environment. In Section 4.3, we

introduce the network and propagation models. In Section 4.4, we analyze the packet-train

concept to improve the underwater acoustic channel efficiency, and cast the optimal packet

size problem for underwater communications when a particular FEC scheme is adopted. In

Section 4.5, we introduce a distributed routing algorithm for delay-insensitive applications,

70

while in Section 4.6 we adapt it to statistically meet the end-to-end delay-sensitive applica-

tion requirements. Finally, in Section 4.7 we show the performance results of the proposed

solutions.

4.2 Related Work

Some recent papers propose network layer protocols specifically tailored for underwater

acoustic networks. In [92], a routing protocol is proposed that autonomously establishes the

underwater network topology, controls network resources, and establishes network flows,

which relies on a centralized network manager running on a surface station. The manager

establishes efficient data delivery paths in a centralized fashion, which allows avoiding

congestion and providing some form of quality of service guarantee. Although the idea is

promising, the performance of the proposed mechanisms has not been thoroughly studied.

In [65], the problem of data gathering for three-dimensional underwater sensor networks

tailored for long-term monitoring missions is investigated at the network layer. A two-

phase resilient routing solution is developed, with the objective of guaranteeing survivabil-

ity of the network to node and link failures. In the first phase, energy-efficient node-disjoint

primary and backup paths are optimally configured, by relying on topology information

gathered by a surface station, while in the second phase paths are locally repaired in case

of node failures. In [94], a vector-based forwarding routing is developed, which does not

require state information on the sensors and only involves a small fraction of the nodes in

routing. The proposed algorithm, however, does not consider applications with different

requirements. In [81], the authors provide a simple design example of a shallow water

network, where routes are established by a central manager based on neighborhood infor-

mation gathered from all nodes by means of poll packets. However, the paper does not

describe routing issues in detail, e.g., it does not discuss the criteria used to select data

paths. Moreover, sensors are only deployed linearly along a stretch, while the characteris-

tics of the 3D underwater environment are not investigated. In [91], a long-term monitoring

71

platform for underwater sensor networks consisting of static and mobile nodes is proposed,

and hardware and software architectures are described. The nodes communicate point-to-

point using a high-speed optical communication system, and broadcast using an acoustic

protocol. The mobile nodes can locate and hover above the static nodes for data muling,

and can perform useful network maintenance functions such as deployment, relocation,

and recovery. However, due to the limitations of optical transmissions, communication is

enabled only when the sensors and the mobile mules are in close proximity.

4.3 Network Models

The 3D underwater network can be represented as a graphG(V, E), whereV = {v1, .., vN}is a finite set of nodes in a finite-dimension 3D volume, withN = |V|, andE is the set of

links among nodes, i.e.,eij equals 1 if nodesvi andvj are within each other’s transmis-

sion range. NodevN (alsoN for simplicity) represents the sink, i.e., the surface station.

Each linkeij is associated with its mean propagation delayT qij and with the standard de-

viation of the propagation delay,σqij. In [90], the underwater acoustic propagation speed

q(z, S, t) [m/s] is accurately modeled as

q(z, S, t) = 1449.05 + 45.7 · t− 5.21 · t2 + 0.23 · t3++(1.333− 0.126 · t + 0.009 · t2) · (S − 35) + 16.3 · z + 0.18 · z2,

(18)

where t = T/10 (T is the temperature in◦C), S is the salinity inppt, and z is the

depth inkm. The above expression provides a useful tool to determine the propagation

speed, and thus the propagation delay, in different operating conditions, and yields values

in [1460, 1520] m/s. Note that all these values, i.e.,eij, T qij andσq

ij, are dependent on the

3D positions of nodesvi andvj (alsoi andj for simplicity in the following). Finally,S is

the set of sources, which includes those sensors that sense information from the underwater

environment and send it to the surface stationN .

The underwater transmission loss describes how the acoustic intensity decreases as an

acoustic pressure wave propagates outwards from a sound source. The transmission loss

72

TL(d, f) [dB] that a narrow-band acoustic signal centered at frequencyf [kHz] experiences

along a distanced [m] can be described by the Urick propagation model [90],

TL(d, f) = χ · 10Log(d) + α(f) · d + A. (19)

In (19), the first term account forgeometric spreading, which refers to the spreading of

sound energy as a result of the expansion of the wavefronts. It increases with the propaga-

tion distance and is independent of frequency. There are two kinds of geometric spreading:

spherical(omni-directional point source, spreading coefficientχ = 2), which characterizes

deep water communications, andcylindrical (horizontal radiation only, spreading coeffi-

cientχ = 1), which characterizes shallow water communications. In-between cases show

a spreading coefficientχ in the interval(1, 2), depending on water depth and link length.

The second term accounts formedium absorption, whereα(f) [dB/m] represents an ab-

sorption coefficient that describes the dependency of the transmission loss on the frequency

band (see Fig. 21). Finally, the last term, expressed by the quantityA [dB], is the so-called

transmission anomaly, and roughly accounts for the degradation of the acoustic intensity

caused by multiple path propagation, refraction, diffraction, and scattering of sound caused

by particulates, bubbles, and plankton within the water column. Its value is higher for

shallow-water horizontal links (up to10 dB), which are more affected by multipath [90].

More details can be found in [28] and [43].

4.4 Packet Train and Optimal Packet Size

In this section, we study the effect of the characteristics of the underwater environment on

the acoustic channel utilization efficiency and provide guidelines for the design of routing

solutions. Specifically, when a common random access technique is adopted to transmit

a data packet in the shared acoustic medium, a trade-off between channel efficiency and

packet size occurs. Conversely, our routing solutions allow achieving two apparently con-

flicting objectives, i.e., increasing the efficiency of the acoustic channel by transmitting

a train of short packetsback-to-back; and limiting the packet error rate by keeping the

73

10−1

100

101

102

10−7

10−6

10−5

10−4

10−3

10−2

10−1

Absorption vs. Frequency

Frequency [kHz]

Abs

orpt

ion

[dB

/m]

Theoretical AbsorptionFisher&Simmons AbsorptionThorp Absorption

Figure 21: Theoretical, Fisher&Simon’s, and Thorp’s medium absorption coefficientα(f)vs. frequencyf ∈ [10−1, 102] kHz

length of the transmitted packets short. The packet-train concept is exploited in the routing

algorithms proposed in this paper.

In particular, we analyze thepacket-trainscheme to enhance the channel efficiency and

derive the optimal packet size. While the optimal packet size at the data link layer in an

underwater channel has been analytically derived in [83], our analysis accounts for cross-

layer interactions with medium access control (MAC) and forward error correction (FEC)

schemes. The packet optimization analysis in [83], in fact, does not consider the additional

overhead caused by the adopted FEC scheme, nor does it evaluate the number of required

packet retransmissions, which depends on the experienced packet error rate (PER), i.e., on

the state of the underwater channel.

4.4.1 Single-packet Transmission Scheme

We consider a shared channel where a device transmits a data packet when it senses the

channel idle, and the corresponding device advertises a correct reception with a short ac-

knowledge (ACK) packet, as shown in Fig. 22. We assume that the payload of the data

packet to be transmitted has sizeLDP bits, while the header has sizeLH

P bits. Moreover,

74

Figure 22: Single-packet transmission scheme

the packet may be protected with a FEC mechanism, which introduces a redundancy of

LFP bits. The ACK packet is assumed to beLA

P bits long. Given a transmission rater, the

packet round-trip timeTRTTP is

TRTTP = TH

P + TDP + T F

P + 2 · T q + T rx−txP + TA

P , (20)

whereTHP , TD

P , T FP , andTA

P are the transmission times of the header, payload, FEC over-

head, and ACK packet, respectively, whileT q is the propagation delay, andT rx−txP is the

time needed to process the packet and switch the circuitry from receiving to transmitting

mode. We define thechannel utilization efficiencyη as

η =1

r· LD

P

NTX · TRTTP

, (21)

whereNTX represents the average number of transmissions needed for the packet to be

successfully decoded at the receiver, i.e.,

NTX =1

1− ψF(LP , LFP , BER)

, (22)

75

whereψF() represents thepacket error rate(PER) given the packet sizeLP and thebit

error rate (BER) on the link, when a FEC schemeF with redundancyLFP is adopted.

Equation (22) assumes independent errors among adjacent packets, which holds when the

channel coherence time is shorter than the average retransmission delay, i.e., the average

time that a sender needs to retransmit an unacknowledged packet. We refer to the expres-

sionr · η = LDP /(NTX ·TRTT

P ) in (21) aseffective link capacitybetween the sender and the

receiver; it represents the average bit rate achievable by a contention-free medium access

control protocol when a single-packet transmission scheme is adopted.

By substituting (20) into (21), we obtain

η =LD

P

NTX · [LDP + LH

P + LFP + LA

P + r · (2dq

+ T rx−txP )]

, (23)

where the propagation delayT q is expressed as the ratio between the distanced between

the sender and the receiver, and the speedq of the signal in the medium, expressed in (18).

Figures 23 and 24 show the channel efficiency (23) for an underwater environment,

where we set the speed of sound in water toq = 1500 m/s (see Section 4.3), and the

transmission rate tor = 50 Kbps [82]. In particular, Fig. 23 refers to transmissions without

forward error correction (i.e.,LFP = 0), while Fig. 24 refers to a(255, 239) Reed-Solomon

(R-S) FEC [68]. Although a thorough study of the performance of different FEC schemes in

the underwater environment is out of the scope of this work, we chose Reed-Solomon FEC

since large block codes are easy to generate and provide excellent burst-error detection

and correcting ability. Note that R-S codes are widely used in conjunction with Viterbi-

decoded convolutional codes to correct the errors made by the Viterbi decoder. In fact,

because of the nonlinear nature of Viterbi decoding, these errors occur in bursts even when

channel errors are random, as with Gaussian noise. The bit error rate on the channel is

assumed to be linearly increasing with decreasing signal-to-noise ratio (SNR), for the sake

of simplicity. In particular, the BER is assumed to range in the interval[10−2, 10−6], as

indicated in [81]. In addition, errors are assumed to be uniformly distributed in time. The

two figures consider a range of distances between100 m and500 m. As can be seen in

76

0 1 2 3 4 5 60

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1Channel Utilization Efficiency vs. Payload Packet Size (NO FEC)

Payload Packet Size [KByte]

Cha

nnel

Util

izat

ion

Effi

cien

cy

@ dist= 100m@ dist= 200m@ dist= 300m@ dist= 400m@ dist= 500m

Figure 23: Underwater and terrestrial channel utilization efficiency for different distances(100 m− 500 m). Underwater channel efficiency vs. packet payload size without FEC

Fig. 23, the maximum channel efficiency is0.25 over a distance of100 m with packet

payload size equal to about0.8 KByte, while it drops below0.05 for distances greater

than200 m. When we apply a (255, 239) R-S FEC technique in the same environment, a

maximum channel utilization efficiency of0.77 can be achieved over100 m with packet

payloads of5 KByte. The efficiency degrades abruptly with increasing distance, and the

optimal packet size, i.e., the packet size that yields maximum channel efficiency on a given

distance, decreases as well. Larger packets tend to improve the channel efficiency; at the

same time, given a bit error rate, the packet error rate increases with increasing packet size,

thus increasing the average number of transmissions for a single packet. Hence, the optimal

packet size is determined as the equilibrium between these two contrasting phenomena.

Figure 25 shows the same phenomena for a terrestrial radio channel, where we set the

propagation speedq to 3 · 108 m/s and the transmission rater to 1 Mbps. The bit error rate

on the channel is assumed to be linearly increasing with decreasing SNR (between10−3

and10−7). With respect to the underwater environment, the channel efficiency values are

higher and degrade more smoothly with increasing distance. In general, the optimal packet

77

0 1 2 3 4 5 60

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1Channel Utilization Efficiency vs. Payload Packet Size ((255,239) R−S FEC)


Cha

nnel

Util

izat

ion

Effi

cien

cy


Figure 24: Underwater and terrestrial channel utilization efficiency for different distances(100 m − 500 m). Underwater channel efficiency vs. packet payload size with(255, 239)Reed-Solomon FEC

0 1 2 3 4 5 60

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1Channel Utilization Efficiency vs. Payload Packet Size (NO FEC)


Cha

nnel

Util

izat

ion

Effi

cien

cy


Figure 25: Underwater and terrestrial channel utilization efficiency for different distances(100 m− 500 m). Terrestrial channel efficiency vs. packet payload size without FEC

78

sizes in this environment are smaller with respect to the underwater case. If we then protect

a packet with FEC techniques, we obtain very high efficiencies (in the order of0.9− 0.95)

for a wide range of distances and packet sizes.

4.4.2 Packet Train and Optimal Packet Size

In the previous section, we considered a shared channel where a device adopts asingle-

packettransmission scheme, i.e., transmits a data packet when it senses the channel idle,

and the corresponding device advertises a correct reception with a short acknowledgement

(ACK) packet. The payload of the data packet to be transmitted is assumed to have size

LDP bits, while the headerLH

P bits. Moreover, the packet may be protected with a FEC

mechanism, which introduces a redundancy ofLFP bits. We observe the following facts

when a single-packet transmission scheme is used in the underwater environment:

• The channel efficiency is very low. This, combined with very low data rates, may be

detrimental for communications. Hence, it is crucial to maximize the efficiency in

exploiting the available resources.

• Underwater communications greatly benefit from the use of forward error correction

(FEC) and hybrid automatic request (ARQ) mechanisms. In fact, combined FEC and

ARQ strategies can consistently decrease the average number of transmissions. The

increasing packet error rate on longer-range underwater links can be compensated for

by either decreasing the packet length, or by applying stronger FEC/ARQ schemes.

• The channel efficiency drops abruptly with increasing distance, and with varying

packet size. In particular, i) the average number of packet retransmissions increases

as the packet size increases, ii) the efficiency decreases as the number of retransmis-

sions increases, and iii) the efficiency increases as the packet payload size increases.

Consequently, the optimal packet size should be determined by considering the trade-

off between channel efficiency and retransmissions.

79

Figure 26: Packet-train performance.Packet-train transmission scheme

To overcome the problems raised by the single-packet transmission scheme, which ulti-

mately lead to low channel efficiencies, we exploit the concept ofpacket train. As shown in

Fig. 26, a packet train is ajuxtapositionof packets, which are transmittedback-to-backby

a node without releasing the channel, in asingle atomic transmission. For delay-insensitive

applications, the corresponding node sends for each train an ACK packet, which can either

cumulatively acknowledgethe whole train, i.e., all the consecutively transmitted packets,

or it canselectivelyrequest the retransmission of specific packets (which are then included

in the next train). In general, a selective repeat approach is to be preferred.

The strategy proposed here allows increasing the efficiency of the acoustic channel by

increasing the length of the transmitted train, without compromising on the packet error

rate, i.e., keeping the transmitted packets short. In other words, wedecouplethe effect of

the packet size from the choice of the length of the train, i.e., the number of consecutive

packets transmitted back-to-back by a node: while the former determines the packet error

rate, the latter can be increased as needed in order to increase the channel efficiency. In

80

fact, the channel efficiency associated with the packet-train scheme is

η = ηT (LT ) · ηP (LP , LFP ). (24)

In (24),ηT (LT ) is the packet-train efficiency, i.e., the ratio between the train payload trans-

mission time and thetrain round-trip timeTRTTT (see Fig. 26) normalized to the bit rate

r,

ηT (LT ) =LD

T

LDT + LH

T + LAT + r · (2d

q+ T rx−tx

T ), (25)

whereLT , LDT , LH

T , andLAT are the train, payload, header, and ACK length, andT rx−tx

T is

the time needed to process the train and switch the circuitry from receiving to transmitting

mode; ηP (LP , LFP ) is the packet efficiency, i.e., the ratio of the packet payload and the

packet size multiplied by the average number of transmissionsNTX such that a packet is

successfully decoded at the receiver, which is defined as

ηP (LP , LFP ) =

LP − LHP − LF

P

NTX · LP

. (26)

Equation (24) accounts for the decoupling between train length, which solely affects the

train efficiencyηT , and choice of the packet structure, which solely affects the packet effi-

ciencyηP .

The optimal packet size(L∗P ) and optimal FEC redundancy(LFP∗) are chosen in such a

way as to maximize the packet efficiencyηP , as cast in the optimal packet size problem.

PsizeP : Optimal Packet Size Problem in UW-ASNs

Given : PTX

max, r, f0, N0, Pr{l}, ψF , ΦM, PERe2emax

Find : L∗P , LFP∗

Maximize : ηP (LP , LFP ) =

LP−LHP −LF

P

NTX ·LP

Subject to :

BER = ΦM(

PTX

max

r ·N0 · TL

); TL =

∫ ∞

0

TL(l, f0) · Pr{l}dl; (27)

81

(Delay − insensitive Applications)

NTX =1

1− ψF(LP , LF

P , BER) ; (28)

(Delay − sensitive Applications)

NTX = 1; (29)

1−[1− ψF

(LP , LF

P , BER)]NHop

max ≤ PERe2emax. (30)

Where:

• P TXi,max [W] is the maximum transmitting power for nodei, andP

TX

max [W] is the aver-

age among all nodes of the maximum transmitting power.

• TL(l, f0) is the transmission loss at distancel and frequencyf0, as described in

Section 4.3, whiler [bps] is the bit rate.

• Pr{l} is the distance distribution between neighboring nodes, which depends on how

nodes are statistically deployed in the volume; for a random 3D deployment,Pr{l}is derived in [59].

• NTX is the estimated number of transmissions of a packet such that it is correctly

decoded at the receiver; (28) assumes independent errors among adjacent packets,

which holds in underwater acoustic channels where the coherence time is shorter

than the average retransmission delay, i.e., the average time that a sender needs to

retransmit an unacknowledged packet.

• ΦM(

PTXmax

r·N0·TL

)represents the average bit error rate(BER) on a link; it is a function

of the ratio between the average energy of the received bitPTX

max/(r · TL) and the

expected noiseN0 at the receiver, and it depends on the modulation schemeM; in

general, the noise has a thermal, an ambient, and a man-made component; studies

of shallow water noise measurements [34] suggest considering an average value of

70 dBµPa for the ambient noise.

82

• ψF(LP , LF

P , BER)

represents the average packet error rate(PER), given the packet

sizeLP , the FEC redundancyLFP , and the average bit error rate(BER), and it de-

pends on the adopted FEC techniqueF .

• PERe2emax is the application maximum allowed end-to-end packet error rate, while

NHopmax is the maximum expected number of hops, function of the network diameter

[74].

The optimal packet sizeL∗P is found by maximizing the packet efficiencyηP in (26) for

different FEC schemesF and code ratesLFP , under a proper set of application-dependent

constraints, i.e.,{(27), (28)} for delay-insensitive applications, and{(27), (29), (30)} for

delay-sensitive applications.

The packet size is optimized given the distance distribution between neighboring nodes

(Pr{l}), which determines the average transmission lossTL, and ultimately theBER,

computed as a functionΦM() of the modulation schemeM and the average signal-to-noise

ratio at the receiver, as formally defined in (27). Thus,PsizeP finds the optimal packet size

and packet FEC redundancy, given the device characteristics(P

TX

max, r, f0, ψF , ΦM)

, the

deployment volume and node density, which impact the distribution between neighboring

nodes(Pr{l}), and the average ambient noise(N0), as

(L∗P , LFP

∗) = argmax(LP ,LF

P ) ηP (LP , LFP ). (31)

Figure 27 shows the underwater packet efficiencyηP when the packet payload size

LDP varies, for different distances (100 m and500 m). In particular, for a volume with an

average node distance of100 m, the highest packet efficiency(η∗P = 0.94) is achieved with

a packet payload size ofLDP∗

= 0.55 KByte and a(255, 251) Reed-Solomon (R-S) FEC,

while for a volume with an average node distance of500 m, the highest packet efficiency

(η∗P = 0.91) is achieved with a packet payload size ofLDP∗

= 0.9 KByte and a(255, 239)

R-S FEC. Figure 28 depicts the train efficiencyηT when the train payload lengthLDT varies,

for different distances (100 m-500 m). Since the train efficiency monotonically increases

83

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1Packet Efficiency vs. Packet Payload Size (@ R−S FEC, dist)

Packet Payload Size [KByte]

Pac

ket E

ffici

ency

@ NO FEC, dist= 100m@ (255,251) R−S, dist= 100m@ (255,239) R−S, dist= 100m@ (255,223) R−S, dist= 100m@ NO FEC, dist= 500m@ (255,251) R−S, dist= 500m@ (255,239) R−S, dist= 500m@ (255,223) R−S, dist= 500m

Figure 27: Packet-train performance.Underwater packet efficiency vs. packet payloadsize for different distances (100 m and500 m)

as the train payload length increases for every distance, we can increase the train efficiency

as needed with the only constraints being that: i) sensor buffer size is limited, and ii) short-

term fairness among sensors competing to access the medium decreases as the train payload

length increases.

To summarize,PsizeP findsoff-line the optimal packet size and packet FEC redundancy

for delay-insensitive and delay-sensitive applications, whereas the distributed algorithms

proposed in the following sections adjuston-linethe strength of the FEC technique by tun-

ing the amount of FEC redundancy according to the dynamic channel conditions, given

the fixed packet sizeL∗P . The choice of a fixed packet size for UW-ASNs is motivated

by the need for system simplicity and ease of sensor buffer management. In fact, a de-

sign proposing per-hop optimal packet size, e.g., solvingPsizeP for any link distance and

use the resultingdistance-dependentoptimal packet size in the routing algorithms, would

encounter several implementation problems, such as the need for segmentation and re-

assembled functionalities that incur tremendous overhead, which are unlikely affordable

by low-end sensors.

84

0 5 10 15 20 25 30 35 40 45 500

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1Packet−Train Utilization Efficiency vs. Packet−Train Payload Length (@ dist)

Packet−Train Payload Length [KByte]

Pac

ket−

Tra

in U

tiliz

atio

n E

ffici

ency


Figure 28: Packet-train performance.Packet-train efficiency vs. packet-train payloadlength for different distances (100 m-500 m)

Throughout this section, we referred to a simple CSMA-like MAC, where a device

transmits a data packet when it senses the shared channel idle, and the corresponding device

advertises correct reception with a short ACK packet. Although we do not advocate this

access scheme for this environment, the results of our analysis are valid when a modified

version of the widely used 802.11 MAC is adopted for UW-ASNs. Moreover, the results

about the channel efficiency motivate the need for the development of a new multiple access

technique for the underwater environment. To this end, we developed a distributed CDMA-

based MAC tailored for the underwater environment, which is described in Chapter 6.

4.5 Delay-insensitive Routing Algorithm

In this section, we introduce a distributed geographical routing solution for delay-insensitive

underwater applications. Most prior research in geographical routing protocols assumes

that nodes can either work in agreedy modeor in arecovery mode. When in greedy mode,

the node that currently holds the message tries to forward it towards the destination. The

recovery mode is entered when a node fails to forward a message in the greedy mode, since

85

none of its neighbors is a feasible next hop. Usually this occurs when the node observes a

void region between itself and the destination. Such a node is referred to asconcavenode.

For example, the GPSR algorithm [46] makes greedy forwarding decisions. When a packet

reaches a concave node, GPSR tries to recover by routing around the perimeter of the void

region. Recovery mechanisms, which allow a packet to be forwarded to the destination

when a concave node is reached, are out of the scope of this work. Hence, the protocol

proposed in this section assumes that no void regions exist, although it can be enhanced by

combining it with one of the existing recovery mechanisms (e.g., [11]).

The objective of our proposed routing solution is to efficiently exploit the underwater

acoustic channel and to minimize the energy consumption. Therefore, the proposed algo-

rithm relies on the packet-train transmission scheme, which is discussed in Section 4.4. In

a distributed fashion, it allows each node tojointly select its best next hop, the transmit-

ted power, and the FEC code rate for each packet, with the objective of minimizing the

energy consumption, while taking the condition of the underwater channel into account.

Furthermore, it tries to exploit those links that guarantee a low packet error rate, in order

to maximize the probability that the packet is correctly decoded at the receiver. For these

reasons, the energy efficiency of the link is weighted with the number of retransmissions

required to achieve link reliability, with the objective of saving energy. We can now cast

the delay-insensitive distributed routing problem.

Pdistinsen: Delay-insensitive Distributed Routing Problem

Given : i, Si, PNi , L∗P , LH

P , Ebelec, r, N0j, P TX

i,max

Find : j∗ ∈ Si ∩ PNi , P TX

ij∗∗ ∈ [0, P TX

i,max], LFP ij∗

∗

Minimize : E(j)i = Eb

ij · L∗PL∗P−LH

P −LFP ij

· NTXij · NHop

ij (32)

Subject to :

Ebij = 2 · Eb

elec +P TX

ij

r; (33)

86

LFP ij = ΨF−1

(L∗P , PERij, Φ

M(

P TXij

N0j · r · TLij

)); (34)

NTXij =

1

1− PERij

; NHopij = max

(diN

< dij >iN

, 1

). (35)

Where:

• L∗P = LHP +LF

P ij +LNP ij [bit] is thefixedoptimal packet size, solution ofPsize

P , where

LHP is thefixedheader size of a packet, whileLF

P ij is thevariableFEC redundancy

that is included in each packet transmitted from nodei to nodej; thus, LNP ij =

L∗P −LHP −LF

P ij is thevariablepayload size of each packet transmitted in a train on

link (i, j).

• Ebelec = Etrans

elec = Erecelec [J/bit] in (33) is thedistance-independentenergy to transit

one bit, whereEtranselec is the energy per bit needed by transmitter electronics (PLLs,

VCOs, bias currents, etc.) and digital processing, andErecelec represents the energy per

bit utilized by receiver electronics. Note thatEtranselec does not represent the overall

energy to transmit a bit, but only the distance-independent portion of it.

• Ebij = 2 · Eb

elec + P TXij /r [J/bit] in (33) accounts for the energy to transmit one bit

from i to j, when the transmitted power and the bit rate areP TXij [W] andr [bps], re-

spectively. The second term represents thedistance-dependentportion of the energy

necessary to transmit a bit.

• TLij in (34) is the transmission loss fromi to j (see Section 4.3).

• NTXij in (32) is the average number of transmissions of a packet sent by nodei such

that the packet is correctly decoded at receiverj.

• NHopij = max

(diN

<dij>iN, 1

)in (35) is the estimated number of hops from nodei to

the surface station (sink)N whenj is selected as next hop, wheredij is the distance

betweeni andj, and< dij >iN (which we refer to asadvance) is the projection of

dij onto the line connecting nodei with the sink.

87

• BERij = φM(Ebrec/N0j) in (34) represents the bit error rate on link(i, j); it is a

function of the ratio between the energy of the received bit,Ebrec = P TX

ij /(r · TLij),

and the expected noise at nodej, N0j, and it depends on the adopted modulation

schemeM.

• LFP ij = ψF−1

(L∗P , PERij, BERij) returns the needed FEC redundancy, given the

optimal packet sizeL∗P , the packet error rate and bit error rate on link(i, j), and it

depends on the adopted FEC techniqueF .

• Si is theneighbor setof nodei, whilePNi is thepositive advance set, composed of

nodes closer to sinkN than nodei, i.e.,j ∈ PNi iff djN < diN .

According to the proposed distributed routing algorithm for delay-insensitive applica-

tions, nodei will selectj∗ as its best next hop iff

j∗ = argminj∈Si∩PNi

E(j)i

∗, (36)

whereE(j)i

∗represents the minimum energy required to successfully transmit a payload

bit from nodei to the sink, taking the condition of the underwater channel into account,

wheni selectsj as next hop. This link metric, objective function (32) inPdistinsen, takes into

account the number of packet transmissions (NTXij ) associated with link(i, j), given the op-

timal packet size (L∗P ), and the optimal combination of FEC(LFP ij

∗) and transmitted power

(P TXij

∗). Moreover, it accounts for the average hop-path length(NHop

ij ) from nodei to the

sink whenj is selected as next hop, by assuming that the following hops will guarantee

the same advance towards the surface station (sink). While this approach to estimate the

number of remaining hops towards the surface station is simple, several advantages can be

pointed out, as described in [88], such as: i) it does not incur any signaling overhead since

it is locally computed and does not require end-to-end information exchange; ii) its accu-

racy increases as the density increases; iii) its accuracy increases as the distance between

the surface station and the current node decreases. For these reasons, we decided to use

88

this method rather than trying to estimate the exact number of hops towards the destination.

Simulation performance in Section 4.7 shows the effectiveness of this choice.

The link metricE(j)i

∗in (36) stands for the optimal energy per payload bit wheni trans-

mits a packet train toj using the optimal combination of powerP TXij

∗ and FEC redundancy

LFP ij

∗ to achieve link reliability, jointly found by solving problemPdistinsen. This interpreta-

tion allows nodei to optimally decouplePdistinsen into twosub-problems: first, minimize the

link metric E(j)i for each of its feasible next-hop neighbors; second, pick as best next hop

that nodej∗ associated with the minimal link metric. This means that the generic nodei

does not have to solve a complicated optimization problem to find its best route towards a

sink. Rather, it only needs to sequentially solve the two aforementioned low-complexity

subproblems, each characterized by a complexityO(|Si ∩ PNi )|, i.e., proportional to the

number of its neighboring nodes with positive advance towards the sink. Moreover, this

operation does not need to be performed each time a sensor has to route a packet, but

only when the channel conditions have consistently changed. To summarize, the proposed

routing solution allows nodei to select as next hop that nodej∗ among its neighbors that

satisfies the following requirements: i) it is closer to the surface station thani, and ii) it

minimizes the link metricE(j)i

∗.

4.6 Delay-sensitive Routing Algorithm

Similarly to the delay-insensitive algorithm introduced in Section 4.5, this algorithm al-

lows each node to distributively select the optimal next hop, transmitting power, and FEC

packet rate, with the objective of minimizing the energy consumption. However, this al-

gorithm includes two new constraints to statistically meet the delay-sensitive application

requirements:

1. The end-to-end packet error rate should be lower than an application-dependent

thresholdPERe2emax;

2. The probability that the end-to-end packet delay be over a delay boundBmax, should

89

be lower than an application-dependent parameterγ.

As a design guideline to meet these requirements, differently from the routing algorithm

tailored for delay-insensitive applications, the proposed algorithm does not retransmit cor-

rupted or lost packets at the link layer. Rather, it discards corrupted packets. Moreover,

it time-stamps packets when they are generated by a source so that it can discard expired

packets. To save energy, while statistically limiting the end-to-end packet delay, we rely on

anearliest deadline firstscheduling, which dynamically assigns higher priority to packets

closer to their deadline. We can now cast the delay-sensitive distributed routing problem.

Pdistsen : Delay-sensitive Distributed Routing Problem

Given : i, Si, PNi , Eb

elec, r, N0j, P TXi,max, ∆B

(m)i , Qij

Find : j∗ ∈ Si ∩ PNi , P TX

ij∗∗ ∈ [0, P TX

i,max], LFP ij∗

∗



P −LFP ij

· NHopij (37)

Subject to :

Ebij = 2 · Eb

elec +P TX

ij

r; (38)

LFP ij = ΨF−1

(L∗P , PERij, Φ

M(

P TXij

N0j · r · TLij

)); (39)

NHopij = max

(diN

< dij >iN

, 1

); (40)

1−(1− PERij

)dNHopij e

≤ PERe2emax; (41)

dij

qij

+ δ(γ) · σqij ≤ min

m=1,..,M

(∆B

(m)i

NHopij

)− Qij − L∗P

r. (42)

In the following, we explain the extra notations and variables used in the problem for-

mulation for delay-sensitive applications:

• M = b(L∗T−LHT )/L∗P c in (42) is thefixednumber of packets transmitted in a train on

each link, whereL∗T andL∗P are the optimal train length and packet size, respectively.

90

• PERe2emax in (41) andBmax [s] are the application-dependent end-to-end packet error

rate threshold and delay bound, respectively.

• ∆B(m)i = Bmax −

[t(m)i,now − t

(m)0

][s] in (42) is the time-to-live of packetm arriving

at nodei, wheret(m)i,now is the arriving time ofm at i, and t

(m)0 is the timem was

generated, which is time-stamped in the packet header by its source.

• Tij = L∗P /r + T qij [s] accounts for the packet transmission delay and the propagation

delay associated with link(i, j), according to Section 4.3; according to measure-

ments on underwater channels reporting symmetric delay distribution of multipath

rays [82], we consider a Gaussian distribution forTij, i.e.,Tij v N (L∗P /r+T q

ij, σqij

2).

• Qi [s] and Qj [s] are the average queueing delays of nodei (at the time the node

computes its next hop) and neighboring nodej, respectively.

• Qij [s] in (42) is the network queueing delay estimated by nodei whenj is selected

as next hop, computed according to the information carried by incoming packets and

broadcast by neighboring nodes, as will be detailed in the next section.

The formulation ofPdistsen is quite similar toPdist

insen, except for two important differ-

ences:

1. The objective function (37) does not includeNTXij as in (32), since no selective packet

retransmission is performed;

2. Two new constraints are included, (41) and (42), which address the two considered

delay-sensitive application requirements, i.e., the end-to-end packet error rate should

be lower than an application-dependent thresholdPERe2emax, and the probability that

the end-to-end packet delay be over a delay boundBmax, should be lower than an

application-dependent parameterγ, respectively.

91

Note that (41) adjusts the packet error ratePERij that will be experienced by packetm

on link (i, j) to respect the application end-to-end packet error rate requirement (PERe2emax),

given the estimated number of hops to reach the sink ifj is selected as next hop (NHopij ).

Interestingly, since the packet is assumed to be correctly forwarded up to nodei, there is no

need to consider the hop count number in (41), i.e., the number of hops of packetm from

the source to the current nodei. In fact, since nodei is assumed to receive the packet, the

conditional probability of it being correct is one. Finally, constraint (42) is mathematically

derived in the following section. The complexity ofPdistsen is O(|Si∩PN

i )|, i.e., proportional

to the number of neighboring nodes with positive advance towards the sink.

4.6.1 Statistical Link Delay Model

In this section, we derive constraint (42) inPdistsen that each link needs to meet to statistically

bound the end-to-end packet delay. To this end, we model the propagation delay of each

link (i, j) as a random variableT qij, with mean equal toT q

ij and varianceσqij

2. The mean

T qij = dij/qij is computed as the ratio of the average multiple path lengthdij and the

average underwater propagation speed of an acoustic wave propagating from nodei to node

j (see Section 4.3). In vertical links, sound rays propagate directly without bouncing on

the bottom or surface of the ocean. Hence, the multipath effect is negligible, anddij ≈ dij.

Conversely, in shallow-water horizontal links, several rays propagate by bouncing on the

bottom and surface of the ocean along with the direct ray. Hence,dij is generally larger

thandij. This is due to the fact that in state-of-the-art underwater receivers, multipath can

be compensated for by waiting for the energy associated with delayed rays. This way, it is

possible to capture the energy spread on multiple paths, and thus guarantee a smaller BER

given a fixed SNR. However, the price for this is that the end-to-end delay may be heavily

affected by the propagation delay of several rays.

By leveraging statistical properties of links, we want the probability that a packet exceed

its end-to-end delay boundBmax to be lower than an application-dependent fixed parameter

92

γ. To achieve this, it should hold

Pr{[

t(m)i,now − t

(m)0

]+ B

(j)iN ≥ Bmax

}= Pr

{B

(j)iN ≥ ∆B

(m)i

}≤ γ, (43)

whereB(j)iN is the expected delay a packet will incur from nodei to the surface stationN

whenj is chosen as next hop, and∆B(m)i = Bmax −

[t(m)i,now − t

(m)0

]is the time-to-live of

packetm arriving at nodei. Nodei can estimate the remaining-path delay by projecting, for

each possible next hopj, the estimated network queueing delayQij and the transmission

delayTij to the remaining estimated hopsNHopij , i.e.,

B(j)iN ≈ (Tij + Qij) · NHop

ij , (44)

where

Qij =t(m)i,now − t

(m)0 −∑

(k,h)∈L(m)i

T kh + Qi + Qj

N(m)HC + 2

. (45)

In (45), the numerator represents the sum of all the queueing delays experienced by packet

m in its pathL(m)i , which includes the links from the source generating packetm to nodei,

and the average queueing delaysQj andQj, computed by nodei and periodically broadcast

by j, respectively. The denominator in (45) represents the number of nodes forwarding the

packet, including nodei, which depends on the hop countN(m)HC , i.e., the number of hops

of packetm from the source to the current node.

By substituting (44) into (43), and by assuming a Gaussian distribution forTij, (43) can

be rewritten as

Pr

{Tij ≥ ∆B

(m)i

NHopij

− Qij

}=

1

2

1− erf

∆B(m)i

NHopij

− Qij − Tij

√2 · σq

ij

≤ γ, (46)

where theerf function is defined as

erf(Γ) =2√π

∫ Γ

0

e−t2dt. (47)

SinceTij = L∗T /r + T qij, andT q

ij = dij/qij, (46) simplifies to

dij

qij

+ δ(γ) · σqij ≤

∆B(m)i

NHopij

− Qij − L∗Pr

, (48)

93

Table 4: Simulation performance parameters

Scen. 1 Scen. 2 Scen. 3Appl. Type [Delay-] insensitive insensitive sensitive

Traffic Type background event-driven event-drivenNo. Sensors - Sources 100− 100 100− 15 100− 15

Volume[m3] 100x100x100 500x500x50 500x500x50Packet Size[Byte] 500 500 100

Rate[bps] 10 150, 300, 600 150, 300, 600Max. Power[W] 0.5 5 5

whereδ(γ) =√

2 · erf−1(1 − 2γ) only depends onγ. In particular,δ(γ) increases with

decreasing values ofγ. In addition, in order to consider, as a precautionary guideline, the

tightest constraint among all those associated with theM packets to be transmitted in a

train, a ‘min’ operator is added, which leads to (42). Note that, while constraint (42) does

not bound the delay of a packet, it tries to increase the probability that a packet reach the

sink within its delay bound. To achieve this, the proposed algorithm only relies on the

past access delay information carried by the packet, and on information about its 1-hop

neighborhood, and not on end-to-end signaling. This information is obtained by broadcast

messages. However, to limit the overhead caused by these messages, each node advertises

its access delay only when it exceeds a pre-defined threshold. Hence, this mechanism

allows the routing algorithm to dynamically adapt to the ongoing traffic and the resulting

congestion.

4.7 Performance Evaluation

In this section, we present the simulation performance of the proposed routing solutions

for delay-insensitive and delay-sensitive UW-ASN applications, introduced in Sections 4.5

and 4.6, respectively.

We extended the wireless package of the J-Sim simulator [1], which implements the

whole protocol stack of a sensor node, to simulate the characteristics of the underwater

environment. In particular, we modeled the underwater transmission loss, the transmission

94

and propagation delays, and the physical layer characteristics of underwater receivers. As

far as the MAC layer is concerned, we adapted the behavior of IEEE 802.11 to the under-

water environment, although we do not advocate this access scheme for this environment.

Firstly, we disabled the RTS/CTS handshaking, as it yields unacceptable delays in a low-

bandwidth high-delay environment. Secondly, we tuned all the parameters of IEEE 802.11

according to the physical layer characteristics. For example, the value of theslot timein the

802.11 backoff mechanism has to account for the propagation delay at the physical layer

[10]. Hence, while it is set to20 µs for 802.11 DSSS (Direct Sequence Spread Spectrum),

we found that a value of0.18 s is needed to allow devices a few hundred meters apart to

share the underwater medium. This implies that the delay introduced by the backoff con-

tention mechanism is several orders of magnitude higher than in terrestrial channels, which

in turn leads to very low channel utilizations. For this reason, we set the values of the

contention windowsCWmin andCWmax [10] to 4 and32, respectively, whereas in 802.11

DSSS they are set to32 and1024. We performed three sets of experiments to analyze the

performance of the proposed routing solutions.

The main parameters differentiating the three experimental scenarios are summarized

in Table 4, while the common parameters are reported hereafter:100 sensors are randomly

deployed in a 3D volume, the initial node energy is set to1000 J, and the available band-

width is50 kHz. In Scenario 1, presented in Section 4.7.1, all deployed sensors are low-rate

sources, which allows us to simulate a low-intensity delay-insensitive background monitor-

ing traffic from the entire 3D volume of100x100x100 m3. Conversely, in Scenarios 2 and

3, presented in Section 4.7.2, we compare the main performance differences between the

delay-insensitive and delay-sensitive routing algorithms, when100 sensors are randomly

deployed in a 3D volume of500x500x50 m3. Note that, differently from Scenario 1, in

these sets of experiments only some sensors inside an event area of radius100 m (centered

inside the 3D monitoring volume) are sources of data packets of size equal to500 Byte and

100 Byte for delay-insensitive and delay-sensitive applications, respectively.

95

0 2 4 6 8 10 120

100

200

300

400

500

600

700

800

900

1000Average Node Residual Energy vs. Time

Time [h]

Ave

rage

Nod

e R

esid

ual E

nerg

y [J

]

Full MetricNo Channel EstimationMinimum Hops

Figure 29: Scenario 1: Delay-insensitive routing.Average node residual energy vs. time,for different link metrics

4.7.1 Scenario 1: Delay-insensitive Background Traffic

We considered100 sensors randomly deployed in a 3D volume of100x100x100 m3, which

may represent a small harbor. We set the maximum transmission power to0.5 W and the

packet size to500 Byte. Moreover, all deployed sensors are low-rate sources, which allows

us to simulate a low-intensity background monitoring traffic from the entire volume, i.e.,

each node transmit a data packet every600 s.

In Fig. 29 we show the average node residual energy over the simulation time. In par-

ticular, we compare the routing performance when three different link metrics are used.

Specifically, theFull Metric (32), introduced in Section 4.5; theNo Channel Estimation,

which does not consider the channel condition, i.e., does not take the expected number

of packet transmissions (NTX) into account; and theMinimum Hops, which simply min-

imizes the number of hops to reach the surface station. When the channel state condition

is considered (Full Metric), consistent energy savings can be achieved, thus leading to pro-

longed network lifetime. In Figs. 30 and 31, we show the average number of hops and the

average packet delays, respectively, when the different link metrics are used. In particular,

96

1 2 30

0.5

1

1.5

2

2.5

3

3.5

4No. of Hops (mean and standard deviation) vs. Link Metric

Full Metric No Channel Estimation Minimum Hops

No.

of H

ops

Figure 30: Scenario 1: Delay-insensitive routing.Average and standard deviation of num-ber of hops vs. time, for different link metrics

0 1 2 3 4 5 6 715

16

17

18

19

20

21

22

23

24

25Average Packet Delay vs. Time

Time [h]

Del

ay [s

]

Full MetricNo Channel EstimationMinimum Hops

Figure 31: Scenario 1: Delay-insensitive routing.Average packet delay vs. time, fordifferent link metrics

97

0 10 20 30 40 50 600

50

100

150

200

250

300Distribution of Data Delivery Delays

Delay [s]

No.

of R

ecei

ved

Pac

kets

Full Metric

Figure 32: Scenario 1: Delay-insensitive routing.Distribution of data delivery delays forthe Full Metric

when the full link metric is adopted, the average end-to-end packet delays are consistently

smaller than with the other metrics, although data paths chosen with the Full Metric are

longer, as shown in Fig. 30. Figure 32 shows the distribution of data delivery delays for

the Full Metric (delay distributions associated with the other two competing metrics are

omitted for lack of space). This can be explained by the lower average node queueing de-

lays and packet transmissions, depicted in Figs. 33 and 34, respectively, observed when

the Full Metric is considered. A lower number of packet transmissions (Fig. 34) is in fact

to be expected, since the metric explicitly takes the state of the underwater channel into

account. Hence, next hops associated to better channels are preferred. This in turn reduces

the average queuing delays (Fig. 33) as packets do not necessarily need to be retransmitted.

4.7.2 Scenarios 2 and 3: Comparison Between Delay-insensitive and Delay-sensitiveEvent-driven Traffic

In this section, we report the main performance differences between the delay-insensitive

and delay-sensitive routing algorithms, when100 sensors are randomly deployed in a 3D

98

1 2 30

2

4

6

8

10

12

14

16

18Packet Queueing Delay (mean and standard deviation) vs. Link Metric


Pac

ket Q

ueue

ing

Del

ay [s

]

Figure 33: Scenario 1: Delay-insensitive routing.Average and standard deviation nodequeueing delays, for different link metrics

1 2 30

1

2

3

4

5

6

7

8No. of Packet Transmissions (mean and standard deviation) vs. Link Metric


No.

of P

acke

t Tra

nsm

issi

ons

Figure 34: Scenario 1: Delay-insensitive routing.Average and standard deviation of num-ber of packet transmissions, for different link metrics

99

volume of500x500x50 m3. Note that, differently from the previous scenario, only some

sensors inside an event area of radius100 m (centered inside the 3D monitoring volume)

are sources of data packets of size equal to500 Byte and100 Byte for delay-insensitive

and delay-sensitive applications, respectively. Moreover, in these simulation scenarios, we

incorporated the effect of the fast fading Rayleigh channel (coherence time set to0.5 s),

to capture the heavy multipath environment in shallow water (depth equal to50 m). In

these sets of experiments we set the maximum transmitting power to5 W, as reported

in Table 4, to account for the larger network diameter than in Scenario 1, i.e.,700 m vs.

170 m. We performed three sets of experiments, each using different source data rates, i.e.,

150, 300, 600 bps.

Figures 35-37 and 38-40 report the end-to-end packet delay and average delay vs. time

for the three considered source rates for delay-insensitive (Scenario 2) and delay-sensitive

(Scenario 3) traffic. From these experiments, we noticed that when the source data rate

increases, the delay-sensitive routing algorithm can statistically bound the end-to-end de-

lay, as shown in Figs. 38-40 where the delays are always smaller than fractions of second.

Conversely, the delay-insensitive routing algorithm results in very high average and peak

delays, as can be seen in Figs. 35-37. The delay-sensitive routing algorithm can statisti-

cally bound the delay since next-hop nodes are chosen in such a way as to control the delay

dispersion on each link, as captured by constraint (42) ofPdistsens cast in Section 4.6. Fur-

thermore, expired packet are discarded in order not to waste bandwidth. This is reported in

Figs. 41-43, which depict generated, received, and dropped delay-sensitive traffic vs. time

for different source rates. Moreover, as opposed to the delay-insensitive routing algorithm,

which manages to deliver all the generated traffic at the expenses of packet delays, cor-

rupted packets carrying delay-sensitive data are not retransmitted, which is reflected in the

small sensor queue size. With this regard, Figs. 44-46 compare the evolution of the queue

and average queue size for the two proposed routing algorithms. While in Scenario 2 tens

of packets are in average enqueued by sensor nodes, in Scenario 3 only a few packets fill

100

0 200 400 600 800 1000 1200 1400 1600 1800

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4Packet Delay and Average Delay vs. Time (@nodes=100)

Time [s]

Pac

ket D

elay

[s]

DelayAverage Delay

Figure 35: Scenario 2: Delay-insensitive routing.Packet delay and average delay vs. timefor source rate equal to150 bps

Table 5: Scenarios 2 and 3: Surface Station and Average Energy per Bit

Source Rate[bps] 150 300 600Scenario 2. Surface Station Energy per Bit[µJ/bit] 8 6.5 7.5Scenario 2. Node Average Energy per Bit[µJ/bit] 7 4 5.5Scenario 3. Surface Station Energy per Bit[µJ/bit] 21 17 18Scenario 3. Node Average Energy per Bit[µJ/bit] 9 6 5

the queues. Table 5 reports the surface station (sink) and average required energy per cor-

rectly received bit for the three different source data rates. Interestingly, in both scenarios

the minimum sink and average energy per bit (in the order of tens ofµJ/bit) is associated

with the intermediate data rate, i.e.,300 bps, when sources generate a consistent amount

of traffic without causing network congestion. In addition, due to packet retransmissions,

in Scenario 2 the energy per bit dissipated by relaying nodes is almost the same as that

required by the surface station to receive and acknowledge incoming packets. Conversely,

a remarkable difference between surface station and average node energy per bit can be

noticed in Scenario 3, where the phenomenon of traffic concentration at the surface station

prevails as far as the total amount of dissipated energy in the network is concerned.

101

0 200 400 600 800 1000 1200 1400 1600 18001

2

3

4

5

6

7Packet Delay and Average Delay vs. Time (@nodes=100)

Time [s]

Pac

ket D

elay

[s]

DelayAverage Delay


0 200 400 600 800 1000 1200 1400 1600 18000

50

100

150

200

250Packet Delay and Average Delay vs. Time (@nodes=100)

Time [s]

Pac

ket D

elay

[s]

DelayAverage Delay


102

0 200 400 600 800 1000 1200 1400 1600 18000.125

0.13

0.135


Time [s]

Pac

ket D

elay

[s]

DelayAverage Delay

Figure 38: Scenario 3: Delay-sensitive routing.Packet delay and average delay vs. timefor source rate equal to150 bps

0 200 400 600 800 1000 1200 1400 1600 18000.16

0.17

0.18

0.19

0.2

0.21


Time [s]

Pac

ket D

elay

[s]

DelayAverage Delay


103

0 200 400 600 800 1000 1200 1400 1600 18000.2

0.22

0.24

0.26

0.28

0.3

0.32

0.34

0.36

0.38


Time [s]

Pac

ket D

elay

[s]

DelayAverage Delay


0 200 400 600 800 1000 1200 1400 1600 18000

50

100

150

200

250

300

350

400

450

500Generated, Received, Dropped, and Lost Traffic vs. Time (@nodes=100)

Time [s]

Tra

ffic

[KB

yte]

Sink Received TrafficGenerated TrafficDropped TrafficLost Traffic

Figure 41: Scenario 3: Delay-sensitive routing.Generated, received, dropped, and losttraffic vs. time for source rate equal to150 bps

104

0 200 400 600 800 1000 1200 1400 1600 18000

100

200

300

400

500

600

700

800

900


Time [s]

Tra

ffic

[KB

yte]



0 200 400 600 800 1000 1200 1400 1600 18000

200

400

600

800

1000

1200

1400

1600

1800


Time [s]

Tra

ffic

[KB

yte]



105

0 200 400 600 800 1000 1200 1400 1600 1800500

1000

1500

2000

2500Queue and Average Queue Size vs. Time (@nodes=100)

Time [s]

Que

ue S

ize

[Byt

e]

Queue SizeAverage Queue Size

Figure 44: Scenarios 2 and 3.Queue and average queue size vs. time; delay-insensitive,source rate equal to300 bps

0 200 400 600 800 1000 1200 1400 1600 18002000

3000

4000

5000

6000

7000

8000

9000


Time [s]

Que

ue S

ize

[Byt

e]


Figure 45: Scenarios 2 and 3.Queue and average queue size vs. time; delay-insensitive,source rate equal to600 bps

106

0 200 400 600 800 1000 1200 1400 1600 1800100

150

200

250

300


Time [s]

Que

ue S

ize

[Byt

e]


Figure 46: Scenarios 2 and 3.Queue and average queue size vs. time; delay-sensitive,source rate equal to600 bps

107

CHAPTER V

A RESILIENT ROUTING ALGORITHM FOR LONG-TERM

UNDERWATER MONITORING MISSIONS

5.1 Preliminaries

The reliability requirements of long-term critical underwater missions, and the small scale

of underwater sensor networks, suggest to devise routing solutions based on some form of

centralized planning of the network topology and data paths, in order to optimally exploit

the scarce network resources. Hence, the proposed solution relies on avirtual circuit rout-

ing technique, where multihop connections are establisheda priori between each source

and sink, and each packet associated with a particular connection follows the same path.

This requires centralized coordination and leads to a less flexible architecture, but allows

exploiting powerful optimization tools on a centralized manager (e.g., the surface station)

to achieve optimal performance at the network layer with minimum signaling overhead.

The remainder of this chapter is organized as follows. In Section 5.2, we propose our

resilient routing algorithm, while in Section 5.3 we show the performance results.

5.2 Basics of the Resilient Routing Algorithm

The proposed routing solution follows atwo-phaseapproach. In thefirst phase, the net-

work manager determines optimalnode-disjoint primaryandbackupmultihop data paths

such that the energy consumption of the nodes is minimized. This is needed because, unlike

in terrestrial sensor networks where sensors can be redundantly deployed, the underwater

environment requires minimizing the number of sensors. Hence, protection is necessary to

avoid network connectivity being disrupted by node or link failures. In thesecond phase,

an on-line distributed solution guarantees survivability of the network, by locally repairing

108

paths in case of disconnections or failures, or by switching the data traffic on the backup

paths in case of severe failures. The emphasis on survivability is motivated by the fact

that underwater long-term monitoring missions can be extremely expensive. Hence, it is

crucial that the deployed network be highly reliable, so as to avoid failure of missions due

to failure of single or multiple devices. The protection scheme proposed can be classified

as a dedicated backup scheme with 1:1 path protection, with node-disjoint paths. Link pro-

tection schemes are not suitable for the underwater environment as they are too bandwidth

consuming [73].

The first phase of the algorithm is described in Section 5.2.1, while the second phase is

presented in Section 5.2.2.

5.2.1 First Phase: Centralized Routing Problem

We formulate the problem of determining optimal primary and backup data paths for UW-

ASNs as anInteger Linear Program(ILP) [4], where:

- eij is a binary variable representing a link that equals 1 iff nodesi and j are within each

other’s transmission range, whilecij is the cost of the link between nodesi andj, i.e., the

energy needed to transmit one bit;

- f1,sij andf2,s

ij are binary variables that equal 1 iff link(i, j) is in theprimaryor in thebackup

data path from the sources to the surface station, respectively;

- ui is the capacity of nodei (number of concurrent flows, ingoing and outgoing, that it can

handle), whilelij is the capacity of link(i, j) (number of concurrent flows that can be trans-

mitted on the link).

The problem can be cast as follows.

109

PRout: Optimal Node-disjoint Routing Problem

Given : G, S, eij , cij , w1, w2, ui, lij

Find : f1,sij

∗, f2,s

ij

∗

Minimize : CT =∑

s∈S∑

(i,j)∈E cij · (w1f1,sij + w2f

2,sij )

Subject to :

∑

j∈V(fx,s

sj − fx,sjs ) = 1, ∀s ∈ S, x = 1, 2; (49)

∑

j∈V(fx,s

Nj − fx,sjN ) = −1,∀s ∈ S, x = 1, 2; (50)

∑

j∈V(fx,s

ij − fx,sji ) = 0, ∀s ∈ S, ∀i ∈ V, i 6= s and i 6= N, x = 1, 2; (51)

fx,sij ≤ eij , ∀s ∈ S, ∀i ∈ V,∀j ∈ V, x = 1, 2; (52)

∑

s∈S

(f1,sij + f2,s

ij ) ≤ lij ,∀i ∈ V, ∀j ∈ V; (53)

∑

s∈S

∑

j∈V

(f1,sji + f2,s

ji ) +∑

j∈V(f1,s

ij + f2,sij )

≤ ui, ∀i ∈ V; (54)

f1,sji +

∑

n∈Vf2,s

ni ≤ 1,∀s ∈ S,∀i ∈ V s.t. i 6= N, ∀j ∈ V. (55)

The objective function of problemPRout aims at minimizing the overall energy consump-

tion as a sum of the energy consumptions of all links that compose the primary and backup

data paths. Two different weightsw1 andw2 are assigned to the primary and backup data

paths, respectively, withw1 +w2 = 1. By increasingw2 we are increasing the weight of the

backup paths in the optimal solution, i.e., we are trying to obtain energy efficient backup

paths. This may worsen the energy consumption of the primary data paths, and should be

done only in scenarios where we expect nodes to fail often, as will be discussed in Section

5.3. In general, we will havew2 << w1. Constraints (49), (50), and (51) express conserva-

tion of flows [4], i.e., each source generates a flow that has to reach the sink. In particular,

constraint (49) imposes that a source node generates a flow, while non-source nodes do not

generate any flow, for primary and backup data path, respectively. Constraint (50) requires

110

that flows generated by each source be collected by the sink. Constraint (51) guarantees

that the balance between incoming and outgoing flows be null for non-source and non-sink

nodes. Constraint (52) forces data paths to be created on links between adjacent nodes.

Constraint (53) ensures that the sum of all flows (primary and backup) transported on a

link do not exceed the link capacity, while constraint (54) imposes that the sum of all flows

(incoming and outgoing, primary and backup) handled by a sensor node do not exceed the

node capacity. Constraint (55) requires the primary and backup paths to be node disjoint. It

can be shown that problemPRout is at least as complex as the Geometric Connected Dom-

inating Set problem, which is proven to be NP-complete [32]. However, it is still possible

to solve the routing problem for networks up to 100 nodes (UW-ASN case).

5.2.2 Second Phase: Localized Network Restoration

In the second phase of the proposed resilient routing algorithm, an on-line distributed so-

lution guarantees survivability of the network, by locally repairing paths in case of discon-

nections or failures. Let us consider the set of connectionsGi for which nodei is either a

sourceor a relay node. We refer to each element inGi asgsi , i.e., a connection generated

by sources and passing through nodei. Hence, nodei is asourcefor the connectiongii,

while it is a relay for each other connection inGi, if any. The connections in this second

group, i.e.,Gi \ gii, are referred to asrelayedconnections fori, while gi

i is referred to as

nativeconnection fori. The restoration of a network connection at nodei is performed in

different ways for native and relayed connections, as discussed in the following.

5.2.2.1 Restoration of a Native Connection

We refer to Fig. 47(a) and consider a nodei as the source of a native connectiongii. Let

us assume thatj is the next hop ofi on its primary path towards the sink. The restoration

process is based on a link quality metricqij that is collaboratively estimated by the two

corresponding nodes at each side of link(i, j), i.e., nodei counts how many ACK timeouts

111

Figure 47: Restoration of a native (a) and relayed connection (b)

expire given a certain number of transmitted packets towardsj. Based on this link qual-

ity, which accounts for both packets corruptions due to channel impairments and receive

failures due to collisions,i performs the following operations.

- If qij < qlow, the link is considered to be in good standing and no action is taken.

- If qij > qhigh, or if no acknowledgement is received fromj, the link is considered

to be impaired altogether. Then,i starts sending the data that it generates to its next hop

m on the backup data path. According toPRout, m is guaranteed to have node capacity

reserved for the backup path ofi towards the source, and capacity is guaranteed to have

been reserved on the backup link(i,m), and on every link on the backup path ofi towards

the surface station.

- If qlow ≤ qij ≤ qhigh, the link is considered to be in an intermediate state. Hence,i

assumes that the quality of link(i, j) on the primary path is degrading. Therefore,i starts

transmitting duplicated packets on the backup path, and, thus, starts computing estimates

qim of the quality of link(i,m). If qlow ≤ qim ≤ qhigh, i keeps transmitting all packets on

both the primary and backup paths to increase the end-to-end reliability. Conversely, if the

112

quality of the backup link is good, i.e.,qim < qlow, nodei tears down the connection on the

primary path to save energy. Finally, ifqim > qhigh, nodei tears down the connection on

the backup path.

As a final remark, if the quality metrics of the links on the primary and backup data

paths are both belowqlow, i.e., qij < qlow andqim < qlow, nodei stops transmitting for a

time Tblackout. After that, it probes the primary and backup links to check if their quality

has improved. If not,i sends the data to a random neighbor in the positive advance set.

5.2.2.2 Restoration of Relayed Connections

Let us consider a relayed connectiongsi ∈ Gi, generated by a sources and relayed by node

i. By referring to Fig. 47(b), let us assume that noden is the next hop of nodei on the

primary data path for the relayed connectiongsi . As in the previous case, nodesj andm are

the next-hop nodes ofi on the primary and backup data paths towards the sink, respectively,

while nodei monitors the quality of link(i, n). However, if the quality of(i, n) degrades,

nodei itself cannot switch the connection on the backup path ofs. In fact, i is not a relay

node for the backup path ofs towards the sink, since primary and backup paths are node

disjoint. Hence, nodei could either inform sources of the relayed connection to switch to

its reserved backup path, or try to locally find an alternate path. Since informing sources

would involve signaling fromi back to the source, incurring in high energy consumption

and delay, we propose a localized solution that tries to take advantage of possibly available

local paths and uses the capacity reserved at sources only in the worst case, when no

capacity is locally available. Hence,i tries to accommodate the relayed connectiongsi

on its own primary or backup data paths, since they are likely to be on energy efficient

paths towards the sink. However, neither the node capacity of next hopsj andm, nor

the capacity of links(i, j) and(i,m) are guaranteed to be sufficient to accommodate the

relayed connection. This happens becausePRout, implemented at the surface station, finds

backup paths on an end-to-end basis (path protection). In other words, the primary path is

113

Table 6: Source Block Probability (SBP) vs. Observation Time

Obs. Time [Days] 20 40 60 80 100SBP(λ = 1 year−1) 0.05 0.18 0.33 0.47 0.55

SBP(λ = 1/2 year−1) 0.02 0.05 0.12 0.18 0.26SBP(λ = 1/3 year−1) 0.01 0.03 0.07 0.10 0.15

protected by a node-disjoint backup path, but not every single link of the primary path is

protected by its own backup path (link protection). Hence, each connection is guaranteed

to have backup capacity reserved only on a path that starts from its source node. Therefore,

i tries to route the failing connection on its primary or backup data paths, but it may fail due

to lack of capacity. Thus, according to the available node and link capacities on links(i, j)

and(i,m), their link qualitiesqij andqim, and the link qualityqin of the original link(i, n),

i decides whether to use one or both of its primary and backup data paths, according to the

rules in Section 5.2.2.1. Note thatn could coincide with eitheri or j, which would restrict

the choice to only two data paths. If at any step in the end-to-end path towards the sink no

node or link capacity is available, an error message is sent back. Each intermediate node

tries to find an alternate path on its own primary and/or backup paths, as explained above. In

the worst case, the source of the relayed connection is reached by the error message, which

triggers a switch to the backup path. Connections that are using the capacity reserved for

other connections are treated as best effort and can be preempted by those connections the

capacity is reserved for.


The optimization problemPRout presented in Section 5.2.1 was implemented in AMPL

[29], and solved with CPLEX [2]. In Figs. 48-50, we compared its performance with a sim-

pler solution, where two node-disjoint shortest weighted paths are calculated with an energy

metric. We considered50 sensors randomly deployed in a 3D volume of500x500x50 m3,

which may represent a small harbor, and we set the bandwidth to50 kbps and the maximum

transmission power to5 W.

114

In particular, Fig. 48 shows the expected energy consumption of the network by weight-

ing the cost of the primary and backup paths with the probability of using each of them.

We adopted a Poissonian model with failure rateλ = 1/2 year−1 to capture the reliabil-

ity of each sensor node (in average one node failure every two years). The expected en-

ergy consumption increases with the observation time, and decreases with increasingw2.

This happens because by increasingw2 the objective function ofPRout weights more the

backup paths. Hence, when failures occur, the connections are switched to backup paths

characterized by lower energy consumption, which ultimately results in decreased energy

consumption. This phenomenon becomes more evident with increasing observation time.

Table 6 shows the source block probability (SBP) with increasing observation time, when

different failure rates are considered. The source block probability is defined as the proba-

bility that a source is able to transmit neither on the primary nor on the backup data path,

since both have at least one failed node. While the source block probability increases with

increasing observation time and failure rateλ, it only slightly depends on the weightw2,

which allows selectingw2 based on energy considerations, irrespective of the required re-

liability. Figure 49 shows a comparison of the average number of hops of source-to-sink

connections on primary and backup paths. Primary paths are shown to be longer (higher

number of hops), and more energy efficient. Figure 50 compares our solution to primary

and backup node-disjoint shortest weighted paths calculated with a hop-distance metric.

While the number of hops of the paths calculated by our solution is doubled, the energy

consumption is lower than with a shortest-hop metric. The cross-over points in Figs. 49

and 50 occur whenw2 = w1 = 0.5, i.e., when the primary and backup paths are equally

weighted to compensate for high failure rates.

As far as the restoration phase in Section 5.2.2 is concerned, we implemented the whole

protocol stack of a sensor node to simulate the underwater transmission loss, the transmis-

sion and propagation delays, the channel fading, and the physical layer characteristics of

underwater receivers. The packet size was set to500 Byte, and the initial node energy to

115

20 30 40 50 60 70 80 90 100 110 1201.085

1.09

1.095

1.1

1.105

1.11

1.115

1.12

1.125

1.13

1.135x 10

4 Expected energy consumption

Observation time [days]

Exp

ecte

d en

ergy

con

sum

ptio

n [n

J/bi

t]

w2=0.01w2=0.1w2=0.2w2=0.3w2=0.4w2=0.45

Figure 48: Expected energy consumption for primary and backup paths

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.52

2.5

3

3.5

4

4.5

5

5.5Average number of hops for primary and backup paths

Backup path weigth (w2)

Ave

rage

num

ber

of h

ops

Primary optimal pathsBackup optimal pathsPrimary shortest pathsBackup shortest paths

Figure 49: Average number of hops for primary and backup paths (optimal and shortestpath)

116

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.51

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8x 10

4 Energy consumption for primary and backup paths

Backup path weigth (w2)

Ene

rgy

cons

umpt

ion

[nJ/

bit]

Primary optimal pathsBackup optimal pathsPrimary shortest pathsBackup shortest paths

Figure 50: Energy consumption for primary and backup path (optimal and minimum-hoppath)

1000 J. All deployed sensors are desynchronized sources, with packet inter-arrival time

equal to60 s, which allows us to simulate alow-intensity monitoring trafficfrom the en-

tire volume. As far as the MAC is concerned, we adapted the behavior of IEEE 802.11,

although we do not advocate this access scheme for this environment. Firstly, we removed

the RTS/CTS handshaking, as it yields high delays in a low-bandwidth high-propagation

delay environment. Secondly, we tuned all the parameters of IEEE 802.11 according to the

physical layer characteristics. For example, while theslot timeis set to20 µs for 802.11

DSSS (Direct Sequence Spread Spectrum), we found that a value of0.18 s is needed to

allow devices a few hundred meters apart to share the underwater medium. We also set

the values of the contention windowsCWmin andCWmax [10] to 8 and64, respectively,

whereas in 802.11 DSSS they are set to32 and1024.

Figures 51-53 and 54-56 show the overall performance of the proposed algorithm, when

sensor-sink primary and backup paths are set according to the first phase of our algorithm

(Section 5.2.1), and sensor failures are locally handled by the restoration algorithm (Sec-

tion 5.2.2). In particular, Fig. 51 reports the generated, received, dropped (due to queue

117

0 500 1000 1500 2000 2500 3000 35000

200

400

600

800

1000


Time [s]

Tra

ffic

[KB

yte]


Figure 51: Generated, received, dropped, and lost traffic vs. time (50 nodes)

overflows), and lost traffic (due to sensor failures), while Fig. 52 shows the time evolution

of the energy per received bit used by the surface station and by an average node. Figure 53

depicts delay and average delay of packets reaching the surface station. The effect of the

fast fading Rayleigh channel (coherence time set to1 s), which models the heavy multipath

UW channel, is captured in Fig. 54, which compares the number of corrupted packets be-

cause of channel impairments to the number of packet collisions and duplications (caused

by lost ACKs). Finally, Fig. 55 depicts the average queue time evolution, while Fig. 56

quantifies the energy increase caused by the routing reconfigurations that are triggered by

the algorithm restoration phase in order to face sensor failures occurring at unpredictable

instants (vertical lines).

118

0 500 1000 1500 2000 2500 3000 35000

0.2

0.4

0.6

0.8

1

1.2x 10

−4Average and Surface Station Energy per Bit vs. Time (@nodes=50)

Time [s]

Ene

rgy

per

Bit

[J/b

it]

Average Energy per BitSurface Station Energy per Bit

Figure 52: Average and surface station used energy per received bit vs. time (50 nodes)

0 500 1000 1500 2000 2500 3000 35000.5

1

1.5

2

2.5

3


Time [s]

Pac

ket D

elay

[s]

DelayAverage Delay

Figure 53: Packet delay and average delay vs. time (50 nodes)

119

0 500 1000 1500 2000 2500 3000 35000

100

200

300

400

500

600No. of Packets Collided, Duplicated, and Corrupted (channel impairments) vs. Time (@nodes=50)

Time [s]

No.

of P

acke

ts

Data Collided PacketsAck Collided PacketsDuplicated PacketsCorrupted Packets

Figure 54: Number of packets collided, duplicated, and corrupted (due to channel impair-ments) vs. time (50 nodes)

0 500 1000 1500 2000 2500 3000 3500500

1000

1500


Time [s]

Que

ue S

ize

[Byt

e]


Figure 55: Queue and average queue size vs. time (50 nodes)

120

0 500 1000 1500 2000 2500 3000 3500

4.25

4.3

4.35

4.4

4.45

4.5

4.55

x 10−3 Expected Routing Energy Cost vs. Time (@nodes=50)

Time [s]

Rou

ting

Ene

rgy

Cos

t [J/

bit]

Routing Energy CostSensor Failure Instants

Figure 56: Expected routing energy increase due to sensor failure vs. time (50 nodes)

121

CHAPTER VI

A CDMA MEDIUM ACCESS CONTROL PROTOCOL FOR

UNDERWATER ACOUSTIC SENSOR NETWORKS

6.1 Preliminaries

A major challenge for the deployment of UW-ASNs is the development of a Medium Ac-

cess Control (MAC) protocol tailored for the underwater environment. In particular, an

underwater MAC protocol should providehigh network throughput, andlow channel ac-

cess delayandenergy consumption, in face of the harsh characteristics of the underwater

propagation medium, while guaranteeingfairnessamong competing nodes.

Code Division Multiple Access (CDMA) is the most promising physical layer and mul-

tiple access technique for UW-ASNs since i) it is robust to frequency-selective fading, ii)

compensates for the effect of multipath by exploiting Rake filters [80] at the receiver, and

iii) allows receivers to distinguish among signals simultaneously transmitted by multiple

devices. As a result, CDMA increases channel reuse and reduces packet retransmissions,

which results in decreased energy consumption and increased network throughput.

For these reasons, in this chapter we introduce UW-MAC, a transmitter-based CDMA

MAC protocol for UW-ASNs that incorporates a novel closed-loop distributed algorithm

to set the optimal transmit power and code length to minimize thenear-far effect1[61].

UW-MAC leverages amulti-user detectoron resource-rich devices such as surface stations

and underwater gateways, and asingle-user detectoron low-end sensors. UW-MAC aims

at achieving three objectives, i.e., guarantee i) high network throughput, ii) low channel

1The near-far effectoccurs when the signal received by a receiver from a sender near the receiver isstronger than the signal received from another sender located further. In this case, the remote sender will bedominated by the close sender. To overcome this problem power control strategies need to be implementedso that signals arrive at the receiver with approximately the same mean power.

122

access delay, and iii) low energy consumption. We prove that UW-MAC manages to si-

multaneously achieve the three objectives in deep water communications, which are not

severely affected by multipath. In shallow water communications2, which may be heavily

affected by multipath, it dynamically finds the optimal trade-off among these objectives.

We also formulate the distributed power and code self-assignment problem to minimize

the near-far effect, and propose a low-complexity yet optimal solution. UW-MAC uses lo-

cally generated chaotic codes to spread transmitted signals on the available bandwidth,

which guarantees a flexible and granular bit rate, secure protection against eavesdropping,

transmitter-receiver self-synchronization, and good auto- and cross-correlation properties

[14]. To the best of our knowledge, UW-MAC is the first protocol that leverages CDMA

properties to achieve multiple access in the bandwidth-limited underwater channel, while

existing papers [31][44] considered CDMA schemes merely from a physical layer perspec-

tive.

The main features that characterize UW-MAC are: i) it provides aunique and flexi-

ble solutionfor different architectures such as static two- and three-dimensional in deep

and shallow water; ii) it isfully distributed, since spreading codes and transmit power are

distributively selected by each sender without relying on a centralized entity; iii) it isintrin-

sically secure, since it uses chaotic codes; iv) itfairly sharesthe bandwidth among active

devices; and v) itefficiently supports multicast transmissions, since spreading codes are

decided at the transmitter side.

The remainder of this chapter is organized as follows. In Section 6.2, we discuss the

suitability of the existing ad hoc and sensor MAC protocols for the underwater environ-

ment. In Section 6.3, we introduce UW-MAC, while in Section 6.4 we formulate the dis-

tributed power and code self-assignment problem. Finally, in Section 6.5, we compare

through simulation UW-MAC with existing MAC schemes for sensor networks tuned for

2In oceanic literature,shallow waterrefers to water with depth lower than100m, while deep waterisused for deeper oceans.

123

the underwater environment.

6.2 Related Work

There has been intensive research on MAC protocols for ad hoc [51] and wireless terrestrial

sensor networks [50] in the last decade. However, due to the different nature of the under-

water environment and applications, existing terrestrial MAC solutions are unsuitable for

this environment. In fact, channel access control in UW-ASNs poses additional challenges

due to the peculiarities of the underwater channel, in particular limited bandwidth, very

high and variable propagation delays, high bit error rates, temporary losses of connectivity,

channel asymmetry, and heavy multipath and fading phenomena. For a thorough discus-

sion on the reasons why several multiple access techniques widely employed in terrestrial

sensor networks such as TDMA, FDMA, and CSMA, are not suitable for the underwater

environment, we refer the reader to [7]. Here, we mainly concentrate on previous work

on CDMA, since this is the most promising physical layer and multiple access technique

for UW-ASNs. In fact, CDMA is i) robust to frequency-selective fading, ii) compensates

for the effect of multipath by exploiting Rake filters [80] at the receiver, and iii) allows

receivers to distinguish among signals simultaneously transmitted by multiple devices. For

these reasons, CDMA increases channel reuse and reduces packet retransmissions, which

results in decreased energy consumption and increased network throughput.

In [31], two spread-spectrum physical layer techniques, namely Direct Sequence Spread

Spectrum (DSSS) and Frequency Hopping Spread Spectrum (FHSS), are compared for

shallow water communications. While in DSSS data is spread to minimize the mutual inter-

ference, in FHSS different simultaneous communications use different hopping sequences

and transmit on different frequency bands. Interestingly, [31] shows that in the underwater

environment FHSS leads to a higher bit error rate than DSSS. Another attractive access

technique combines DSSS CDMA with multi-carrier transmissions [44], which may offer

higher spectral efficiency than its single-carrier counterpart. This way, high data rate can be

124

supported by increasing the duration of each symbol, which reduces Inter Symbol Interfer-

ence (ISI). However, multi-carrier transmissions may not be suitable for low-end sensors

because of their high complexity. Therefore, we focus on single-carrier CDMA to keep

the complexity of resource-limited sensor transceivers low. Remarkably, the above papers

[31][44] merely consider CDMA from a physical layer perspective, i.e., they analyze the

suitability of different forms of CDMA-based transmission techniques with respect to the

challenges raised by the underwater channel. Instead, our contribution is to develop a dy-

namic multiple access protocol for UW-ASNs that efficiently shares the scarce underwater

channel bandwidth by fully leveraging the CDMA medium access properties.

In [76], a solution for underwater networks with AUVs was devised. The scheme is

based on organizing the network in multiple clusters, each composed of adjacent vehi-

cles. Interference among different clusters is minimized by assigning orthogonal spreading

codes to different clusters. Inside each cluster, TDMA is used with long band guards

to overcome the effect of the propagation delay. Since vehicles in the same cluster are

assumed to be close to one another, the negative effect of the very high underwater prop-

agation delay is limited. The proposed solution, however, assumes a clustered network

architecture and proximity among nodes within the same cluster, while we seek a more

general and flexible solution suitable for different network sizes and architectures.

In [60], Slotted FAMA, a protocol based on a channel access discipline called Floor

Acquisition Multiple Access (FAMA) is proposed. It combines both carrier sensing (CS)

and a dialogue between the source and receiver prior to data transmission. During the initial

dialogue, control packets are exchanged between the source node and the intended desti-

nation node to avoid multiple transmissions at the same time. Time slotting eliminates the

asynchronous nature of the protocol and the need for long control packets, thus providing

energy savings. However, guard times should be inserted in the time slot to account for

any system clock drift. In addition, because of the high underwater acoustic propagation

delay, the handshaking mechanism may lead to low system throughput, and the CS scheme

125

may sense the channel idle while a transmission is still taking place, thus causing packet

collisions.

In [35], the impact of the large propagation delay on the throughput of selected clas-

sical MAC protocols and their variants is analyzed, and PCAP, Propagation-delay-tolerant

Collision Avoidance Protocol, is introduced. Its objective is to fix the time spent on set-

ting up links for data frames, and to avoid collisions by scheduling the activity of sensors.

Although PCAP offers higher throughput than widely used conventional protocols for wire-

less networks, it does not provide a flexible solution for applications with heterogeneous

requirements.

A distributed CSMA-based energy-efficient MAC protocol for the underwater environ-

ment was recently proposed in [75]. Its objective is to save energy based on sleep periods

with low duty cycles. The solution is tied to the assumption that nodes follow sleep periods,

and is aimed at efficiently organizing the sleep schedules. Conversely, we are interested in

optimizing the utilization of the shared medium to maximize throughput and reduce the en-

ergy consumption. Moreover, while our proposed MAC protocol may be enhanced with a

sleep schedule algorithm for dense deployment scenarios, we decided not to incorporate it

in the basic protocol to make it suitable for a variety of traffic, architecture, and deployment

scenarios.

6.3 UW-MAC: A Distributed CDMA MAC for UW-ASNs

6.3.1 Basics

UW-MAC is a transmitter-based Direct Sequence CDMA (DS-CDMA) scheme for UW-

ASNs that implements a novelclosed-loop distributed algorithmto set the optimal transmit

power and code length to minimize the near-far effect. UW-MAC leverages amulti-user de-

tectoron resource-rich devices such as uw-gateways and surface stations, and asingle-user

detectoron low-end sensor nodes. In DS-CDMA communication systems, the information-

bearing signal is directly multiplied by a spreading code with a larger bandwidth than the

126

data. The receiver despreads the transmitted spread spectrum signal using a locally gener-

ated code sequence. To perform the despreading operation, the receiver must know the code

sequence used to spread the signal. Moreover, the received signal and the locally generated

code must be synchronized. This synchronization must be accomplished at the beginning

of the reception and maintained until the whole signal has been received. In a DS-CDMA

scheme the major problem encountered is the Multiuser Access Interference (MAI), which

is caused by simultaneous transmissions from different users. In fact, the system efficiency

is limited by the total amount of interference and not by the background noise exclusively

[17]. Therefore, low cross-correlation between the desired and the interfering users is im-

portant to reduce the MAI. Moreover, adequate auto-correlation properties are required for

reliable initial synchronization. In fact, large sidelobes of the autocorrelation function can

easily lead to erroneous code synchronization decisions. In addition, good autocorrelation

properties of the spreading code result in a better resolution of the multipath components of

a spread spectrum signal. Unfortunately, cross-correlation and autocorrelation properties

cannot be optimized simultaneously.

Single-user detection (SUD) devices use low-cost conventional Rake receivers [80] to

detect one user without regard to the existence of other users, which are treated as noise. Al-

though these receivers leverage multipath diversity, there is no sharing of multi-user infor-

mation or joint signal processing. Conversely, multi-user detection (MUD) devices simul-

taneously despread signals from several users. Consequently, the two problems ofchannel

equalizationandsignal separationare jointly solved to increase the signal-to-interference-

plus-noise ratio (SINR) and achieve good performance. MUD techniques have been studied

extensively and a number of optimal and suboptimal algorithms have been proposed [52].

These techniques, however, usually require channel estimation and knowledge of all the

active user spreading codes, and have considerable computational cost. While this may

be feasible for the surface station, and in general for resource-rich devices such as uw-

gateways and AUVs, it contrasts with the desire to keep low-end sensors simple and power

127

efficient. For these reasons, MUD techniques may be suitable for resource-rich devices

such as uw-gateways and surface stations, but not for low-end underwater sensors. Thus,

UW-MAC relies on low-complexity single-user detectors on low-end underwater sensor

nodes.

6.3.2 Protocol Description

Our proposed distributed closed-loop solution aims at setting the optimal combination of

transmit power and code length at the transmitter side relying on local periodic broadcasts

of MAI values from active nodes, as shown in Fig. 57. Here, nodei needs to transmit a

data packet toj, without impairing ongoing communications fromh to k and fromt to

n. Since the system efficiency is limited by the amount of total interference, it is crucial

for i to optimize its transmission, in terms of transmit power and code length, to limit the

near-far problem. The power and code self-assignment problem is formally introduced in

Section 6.4, where a distributed low-complexity yet optimal solution is proposed.

In UW-MAC, nodesrandomly accessthe channel transmitting a short header called the

Extended Header (EH). The EH, of sizeLEH bits, is sent using acommon chaotic codecEH

known by all devices at the maximum rate (minimum code length). Senderi transmits to

its next hopj, locateddij meters apart, the short header EH. The EH contains information

about the final destination, i.e., the surface station, the chosen next hop, i.e., nodej, and

the parameters thati will use to generate thechaotic spreading codefor the actual data

packet, of sizeLD bits, that j will receive fromi. Immediately after the transmission of

the EH,i transmits the data packet on the channel, which is characterized by a raw chip

rater [cps] and sound velocityq ≈ 1500 m/s, using the optimal transmit powerP ∗ij [W]

and code lengthc∗ij set by the power and code self-assignment algorithm. If no collision

occurs during the reception of the EH, i.e., ifi is the only node transmitting an EH in the

neighborhood of nodej, j will be able to synchronize to the signal fromi, despread the

EH using the common code, and acquire the carried information. At this point, if the EH

128

Figure 57: Data and broadcast message transmissions

is successfully decoded, receiverj will be able to locally generate the chaotic code that

used to send its data packet, and set its decoder according to this chaotic code in such

a way as to decode the data packet. Oncej has correctly received the data packet from

i, it acknowledges it by sending an ACK packet, of sizeLA bits, to j using codecA. In

casei does not receive the ACK before a timeoutTout expires, it will keep transmitting the

packet until a maximum transmission numberNTmax is reached. The timeout must be tuned

considering the long propagation and transmission delays, i.e.,Tout ≥ cEH · LEH/r + cij ·LD/r + 2dij/q + cA · LA/r. Algorithm 2 reports the pseudo-code executed by senderi.

Note that if senderi does not have updated information about the MAI inj, it increases

the code length every time a timeout expires to improve the probability that the packet is

successfully decoded, i.e.,cNT

ij

ij = min [cNT

ij−1

ij · 2β, cmax], where1 ≤ NTij ≤ NT

max andβ ∈R+. As will be shown in Section 6.5, this mechanism guarantees stability and decreases

transients, although it temporarily decreases the transmission data rate.

129

Algorithm 2 UW-MAC pseudo-code executed by senderi

Send an EH packet to nodej using common codecEH

ExecutePower and Code Self-assignment Algorithm⇒ (c∗ij, P∗ij,m

∗ij)

Generate chaotic codec∗ij and spread the data packetTransmit the data packet using powerP ∗

ij and marginm∗ij

6.4 Power and Code Self-assignment Problem

Hereafter, we formulate the distributed power and code self-assignment problem, and pro-

pose a low-complexity yet optimal closed-loop solution. An open-loop power control al-

gorithm that does not rely on feedback from the receiver would rely on the symmetric link

assumption, which does not hold in the underwater environment.

6.4.1 Deep Water Channels

We consider a deep water acoustic channel, which is not severely affected by multipath,

where the transmission lossTLij that a narrow-band acoustic signal centered at frequency

f [kHz] experiences between nodesi andj at distanced [m] is described by the Urick prop-

agation model [90],TLij = d2ij ·10[α(f)·dij+A]/10, whereα(f) [dB/m] represents themedium

absorption coefficient, andA ∈ [0, 5] dB is the so-calledtransmission anomaly, which ac-

counts for the degradation of the acoustic intensity caused by multiple path propagation,

refraction, diffraction, and scattering of sound.

Each nodei needs to i) limit the near-far effect when it transmits toj and ii) avoid

impairing ongoing communications. These constraints are mathematically expressed by

the following equations,

N0+IjPij

TLij

≤ wij · Φ(BERj)

N0+Ik+Pij

TLik

Sk≤ wtkk · Φ(BERk), ∀k ∈ Ki.

(56)

In (56), N0 [W] is the average noise power,Ij and Ik [W] are the MAI at nodesj and

k ∈ Ki, with Ki being the set of nodes whose ongoing communications may be affected

by nodei’s transmit power. Then,wij andwtkk are the bandwidth spreading factors of

130

the ongoing transmissions fromi to j and fromtk to k, respectively, wheretk is the node

transmitting tok. Furthermore,Pij [W] represents the power transmitted byi to j when an

ideal channel (without multipath, i.e.,A = 0 dB) is assumed, i.e., when no power margin

is considered to face the fading dips. Finally,TLij andTLik are the transmission losses

from i to j and fromi to k ∈ Ki, respectively, whileSk [W] is the power of the signal that

receiverk is decoding, andΦ() is the MAI threshold, which depends on the target bit error

rate(BER) at the receiver node (see [61]). We will denote the noise and MAI power of

a generic noden asNIn = N0 + In, and the normalized received spread signal, i.e., the

signal power after despreading, asSn = Sn · wtnn · Φ(BERn).

The first constraint in (56) states that the SINR−1 at receiverj needs to be below a

certain threshold, i.e., the powerPij transmitted byi needs to be sufficiently high to allow

receiverj to successfully decode the signal, given its current noise and MAI power level

(NIj). The second constraint in (56) states that the SINR−1 at receiversk ∈ Ki must

not be above a threshold, i.e., the powerPij transmitted byi must not impair the ongoing

communications toward nodesk ∈ Ki, given their normalized received user spread signals

(Sk), and noise and MAI level(NIk). By combining the constraints in (56), we obtain the

following compact expression,

NIj ·TLij

wij ·Φ(BERj)≤ Pij ≤ mink∈Ki

[(Sk −NIk) · TLik

]. (57)

Consequently, to set the transmit powerPij and spreading factorwij, nodei needs to lever-

age information on the MAI and normalized receiving spread signal of neighboring nodes.

This information is broadcast periodically by active nodes, as depicted in Fig. 57. In par-

ticular, to limit such broadcasts, a generic noden transmits only significant values ofNIn

andSn, i.e., out of predefined tolerance ranges.

To save energy, nodei will select a transmit powerPij and a code lengthcij in such a

way as to satisfy the set of constraints in (57) and to minimize the energy per bitEbij(Pij, cij) =

(Ptx + Pij) · cij/r [J/bit]. Here,Ptx [W] is adistance-independentcomponent accounting

for the power needed by the transmitting circuitry, andr [cps] theconstantunderwater chip

131

rate, which is proportional to the available acoustic spectrumB [Hz] and to the modulation

spectrum efficiencyηB, i.e.,r = ηB · B. SinceEbij decreases as transmit power and code

length decrease, and since the relation between the spreading factorwij and the code length

cij depends on the family of codes, i.e.,wij = WC(cij), the optimal solution isc∗ij = cmin

andP ∗ij = NIj · TLij/[α · cmin · Φ(BERi)], where we assumed the spreading factor to

be proportional to the code length, i.e.,wij = α · cij. Note that this solution achieves the

three objectives of minimizing the energy per bitEbij that i needs to successfully commu-

nicate withj in the minimum possible time, i.e., minimize the energy consumption while

transmitting at the highest possible data rate, i.e.,r/cmin.

6.4.2 Shallow Water Channels

We assume now that the channel is heavily affected by multipath (saturated condition, see

[70]) as it is often the case in shallow water [7]. In this environment, the signal fading can be

modeled by a Rayleigh r.v., which accounts for aworst-case scenario, and the transmission

loss betweeni andj is TLij · ρ2, whereTLij = dij · 10[α(f)·dij+A]/10, with A ∈ [5, 10] dB,

andρ has a unit-mean Rayleigh cumulative distributionDρ(ρ) = 1 − exp(−πρ2/4). Let

us define thesignal transmission marginfor link (i, j) asmij, whereP ∗ij · m2

ij [W] is the

actual transmit power, whileP ∗ij [W] represents the optimal transmission power in an ideal

channel, as introduced in Section 6.4.1, i.e., the transmit power before applying the margin

to face the fading dips. The packet error ratePERij experienced on link(i, j) when sender

i transmits powerP ∗ij ·m2

ij can be defined as the probability that the received power at node

j be smaller than that required in an ideal channel where no multipath is experienced, i.e.,

PERij = Pr

{P ∗

ij ·m2ij

TLij · ρ2<

Pij∗

TLij

}= Pr

{ρ ≥ mij

}= 1−Dρ(mij) = exp

(− πm2

ij

4

).

(58)

Hence, the average number of transmissions of a packet such that receiverj correctly

decodes it when it is sent with signal transmission marginmij isNTij (mij) = [1−PERij]

−1 =

Dρ(mij)−1. This relation assumes independent errors among adjacent packets, which holds

132

when the channel coherence time is shorter than the retransmission timeout, i.e., the time

before retransmitting an unacknowledged packet. We can now cast the power and code

self-assignment optimization problem in a Rayleigh channel.

P: Power and Code Self-assignment Optimization Problem

Given : Pmax, r, TLij, NIj, BERj; Sk, NIk,∀k ∈ Ki

Find : c∗ij ∈ [cmin, cmax], P ∗ij ∈ R+, m∗

ij ∈ R+

Min. : Ebij(cij, Pij,mij) =

(Ptx+Pij ·m2ij)·cij

r·NT

ij (mij)

Subject to :

NTij (mij) = Dρ(mij)

−1 =

[1− exp

(− πm2

ij

4

)]−1

; (59)

Pminij (cij) ≤ Pij ≤ min [Pmax

ij , Pmax]; (60)

Pminij (cij) ≤ Pij ·m2

ij ≤ min [Pmaxij , Pmax]; (61)

where

Pminij (cij) =

NIj · TLij

α · cij · Φ(BERj)=

Γij

cij

, (62)

Γij =NIj · TLij

α · Φ(BERj), (63)

Pmaxij = min

k∈Ki


]. (64)

Note that, in constraints (60) and (61), the transmit powerlower bound, Pminij , is afunc-

tion that depends on the chosen code lengthcij, which is a solution variable ofP, whereas

the transmit powerupper bound, min [Pmaxij , Pmax], is aconstantonly depending on the

node maximum transmit power (Pmax) and on the broadcast MAI(NIk) and normalized

received spread signal(Sk).

While P may seem a fairly complex optimization problem, it admits a low-complexity

yet optimal closed-form solution. To find it, we rely on a property of the objective function,

i.e., the minimum energy per bitEbij monotonically decreases asPij and the code lengthcij

decrease.P mayadmit a feasible solution if in (60)Pminij (cij) ≤ min [Pmax

ij , Pmax] holds,

133

i.e., if cij ≥ Γij

min [P maxij ,P max]

. Consequently, to minimize the objective function, we want the

optimal code length3 c∗ij to be

c∗ij = max

[min

[γ·Γij

min [P maxij ,P max]

, cmax

], cmin

], (65)

whereγ is a margin on the code length aimed at absorbing information inaccuracy. By

substituting (65) into (62), given (60), we obtain the optimal transmit powerbeforeapplying

the margin to the channel,P ∗ij, as

P ∗ij = min

[Γij

c∗ij, Pmax

]. (66)

Finally, by substituting (65) and (66) into the objective function, we obtain the energy per

bit as a function of the margin only,

Ebij(mij) =

Ptx·c∗ij+Γij ·m2ij

r·[

1−exp

(−πm2

ij4

)] ,(67)

which can then be minimized to obtain the optimal marginm∗ij as numeric solution of the

following equation

dEbij

dmij= 0; ⇒ m∗

ij2

4+

πPtxc∗ij4Γij

+ 1 = exp(

πm∗ij

2

4

). (68)

Note thatP is feasible iff the optimal solution(c∗ij, P∗ij,m

∗ij) meets constraint (61), i.e.,

iff P ∗ij · m∗

ij2 ≤ min [Pmax

ij , Pmax]. Otherwise, an energy-efficient suboptimal solution,

(c+ij, P

+ij , m+

ij), would bec+i = cmax andP+

ij ·m+ij

2= min [Pmax

ij , Pmax].

The computational complexity of the proposed optimal closed-form solution is very

low since the most computation-intense operation is finding the solution to (68). Many nu-

merical algorithms such as theNewton descending approximationcan be effectively used.

Moreover, a transmitting node does not have to adjust its transmit power and code length

every time it needs to communicate, but only if any of the inputs ofP has consistently

changed. Not only does this make the computational burden on low-end sensors easily

3Note that, by usingchaotic codesas opposed topseudo-random sequences, a much higher granularity inthe choice of the code length can be achieved; code lengths, in fact, do not need to be a power of2.

134

0 5 10 15 20 25 30 35 400

2

4

6x 10

−4 Minimum Energy per Bit vs. Code Length (@Rayleigh Channel, d= 100m)

Code Lenght

Min

imum

Ene

rgy

per

Bit

[J/b

it]

NI= 0.0001WNI= 0.00048WNI= 0.00086WNI= 0.00124WNI= 0.00162WNI= 0.002W

Figure 58: Minimum energy per bit vs. code length (Rayleigh Channel)

affordable, but it also helps reach system stability while limiting the signaling overhead, as

will be shown in Section 6.5.

Differently from the deep water case, the energy per bit in a Rayleigh channel sky-

rockets when an adequate power margin is not used, because of the high number of packet

retransmissions, as accounted by (59). Moreover, a trade-off between the optimal trans-

mit power and code length occurs, which suggests that it is not always possible tojointly

achieve the highest data rate and the lowest energy consumption, as it is possible in a chan-

nel that is not affected by multipath.

This non-trivial result is confirmed by Fig. 58, where the minimum energy per bit in a

Rayleigh channel under different MAI power levels(NIj) at receiverj is reported, when

the code lengthcij ranges fromcmin = 4 to cmax = 40. As previously anticipated, when

the MAI at the receiver side is higher than a certain threshold(NIj ≥ 1.24 mW) it is

not possible anymore to select the highest data rate, i.e., the shortest code, to achieve the

minimum energy per bit. Conversely, with low MAI at the receiver, this twofold objective

can still be achieved. In fact, the lowest three monotonic curves in Fig. 58 show that the

minimum energy per bit is achieved when the code length is minimum(c = cmin), i.e.,

135

when the transmit rate is maximum. Conversely, the upper curves have minima that are not

associated with the lowest code length, which shows the need to trade off between energy

consumption and transmission rate.


In this section, we discuss performance results of UW-MAC, presented in Section 6.3, for

three different UW-ASN architectures described in [7], the2D deep water, the3D shallow

water, and the3D with AUVs. In addition, we evaluate the added benefit in terms of energy

consumption, channel access delay, and network throughput of multi-user detectors over

single-user detectors, introduced in Section 6.3, in a wide variety of conditions and scenar-

ios to capture relevant underwater setups. To accomplish this, we evaluate two versions of

our proposed MAC solution. In particular, we refer toUW-MACsglas the case where all

nodes implement a single-user detector, and toUW-MACmltas the case where resource-

rich devices such as uw-gateways and surface stations implement a multi-user detector,

while low-end sensor nodes implement a single-user detector.

We implemented the entire protocol stack of a sensor node to simulate the characteris-

tics of the underwater environment. In particular, we modeled the underwater transmission

loss, the transmission and propagation delays, and the physical layer characteristics of un-

derwater receivers. We decided to implement neither Slotted FAMA [60] nor the MAC

protocol proposed in [75] since their objectives differ from those of our CDMA MAC solu-

tion, as described in Section 6.2, and a fair comparison is not possible. Rather, we compare

the two versions of UW-MAC, UW-MACsgl and UW-MACmlt, with four existing random

access MAC protocols, which we optimized to the underwater environment, i.e., CSMA,

CSMA with power control (CSMApw), IEEE 802.11, and ALOHA. In particular, in IEEE

802.11 the value of the slot time in the backoff mechanism has to account for the propa-

gation delay at the physical layer. Hence, while it is set to20 µs for 802.11 DSSS, a value

of 0.18 s is needed to allow devices a few hundred meters apart to share the underwater

136

medium. This implies that the delay introduced by the backoff contention mechanism is

several orders of magnitude higher than in terrestrial channels, which in turn leads to very

low channel utilization efficiencies. In addition, we set the values of the contention win-

dowsCWmin andCWmax to 8 and64, respectively, whereas in 802.11 DSSS they are set

to 32 and1024, and the binary backoff coefficient to1.5, whereas it is usually set to2 in

terrestrial implementations.

In all the simulation scenarios, we considered a common set of parameters, which is

reported in the following, whereas specific parameters for each architecture are reported in

the appropriate section. We set the chip rater to 100 kcps, the minimum code lengthcmin

to 4 and the maximumcmax to 40, the maximum transmission powerPmax to 10 W, the

data packet size to250 Byte, the control and header packet size to10 Byte, the initial node

energy to1000 J, the queue size to10 kByte, the available acoustic spectrum to50 kHz,

and the transmission anomalies caused by multipath in deep and shallow water to0 dB and

5 dB, respectively. Moreover, all deployed sensors are sources, with packet inter-arrival

time equal to20 s, which allows us to simulate alow-intensity background monitoring

traffic from the entire volume toward the surface station, which is centered on the surface

of the underwater volume. Finally, we adopted the geographical routing algorithm tailored

for UW-ASNs, which we proposed in [66], according to which each node selects its next

hop with the objective of minimizing the energy consumption. Simulation results presented

in the next sections are averaged on several experiments to obtain small95% confidence

relative intervals, which are showed in the figures.

6.5.1 Two-dimensional Deep Water UW-ASNs

We considered a variable number of sensors (from10 to 50) randomly deployed on the bot-

tom of a deep water volume of500x500x500 m3. The underwater gateways are randomly

deployed on the bottom as well, and their number is varied in such a way as to be20%

137

of the total number of deployed sensors. The antenna gain at the transmitting and receiv-

ing side of a vertical link is set to10 dB, according to data sheets of available long-haul

hydrophones (underwater microphones).

Figures 59 and 60 depict the average packet delay and energy per received bit in the

simulation transient state when30 sensors are deployed. The proposed UW-MAC proto-

col versions outperform the competing MAC schemes in terms of both delay (one order of

magnitude) and energy consumption (25µ J/bit vs. 45µ J/bit and over), although the ex-

tremely harsh scenario leads to delays in the order of seconds and high energy per bit for all

the MAC schemes. Figures 61 and 62-64 show the overall performance of the competing

MAC protocols when the number of deployed sensors and uw-gateways increases. Figure

61 shows that both UW-MACsgl and Uw-MACmlt have a much smaller average packet

delay than the competing schemes. In particular, it is pointed out that the RTS/CTS hand-

shaking of 802.11 yields high delays in the low-bandwidth high-delay underwater environ-

ment. As far as the energy per successfully received bit is concerned, we note that our MAC

solutions are the most energy efficient (Fig. 62). Surface sinks, however, are resource-rich

devices since they are in general endowed with higher capacity batteries. Moreover, bat-

teries on surface stations can be recharged through renewable energy sources, whereas the

energy of underwater sensors is limited and usually batteries cannot be easily recharged,

also because solar energy cannot be exploited.

The highest successfully received number of packets is associated with our UW-MACmlt

(Fig. 63), which takes advantage of its multi-user receiving capabilities. All the schemes

relying on carrier sense (CSMA, CSMApw, and 802.11) perform poorly since this mech-

anism prevents collisions with the current transmissions only at the transmitter side. To

prevent collisions at the receiver side it would be necessary to add a guard time between

transmissions dimensioned according to the maximum propagation delay in the network,

which would make the protocols dramatically inefficient in the underwater environment.

138

Consequently, thehidden terminaland theexposed terminalproblems4 are the main causes

for the low performance of MAC schemes relying on carrier sense. Figure 64 quantifies the

dramatic decrease in terms of data packet collisions of our proposed UW-MAC schemes,

which is motivated by the very low collision probability of the small EH randomly access-

ing the channel. Conversely, ALOHA experiences a high number of packet collisions since

it directly accesses the medium whenever there is data to be transmitted. In the underwa-

ter environment, ALOHA is often affected by low efficiency, mainly because of the low

acoustic propagation speed. Moreover, the need for retransmissions increases the power

consumption of sensors, as confirmed in Fig. 62, which ultimately reduces the network

lifetime.

As a final remark, the use of contention-based techniques that rely on handshaking

mechanisms such as RTS/CTS in shared medium access (e.g., MACA [45], IEEE 802.11)

is impractical in underwater, for the following reasons: i) large delays in the propagation

of RTS/CTS control packets lead to low channel utilization efficiency and throughput; ii)

because of the high underwater acoustic propagation delay, when carrier sense is used, it

is more likely that the channel will be sensed idle while a transmission is taking place,

since the signal may not have reached the receiver yet; iii) the variability of delay in hand-

shaking packets makes it impractical to accurately predict the start and finish time of the

transmissions of other nodes.

6.5.2 Three-dimensional Shallow Water UW-ASNs

We considered a variable number of sensors (from10 to 50) randomly deployed in the 3D

shallow water with volume of500x500x50 m3, which may represent a small harbor. We

modeled the multipath phenomenon by considering a worst-case scenario consisting of a

saturated fast fading Rayleigh channel with coherence time equal to1 s. As compared to

4Thehidden terminal problemarises when the channel is sensed free by the sender although the receiveris already receiving another packet from another node, while theexposed terminal problemis encounteredwhen the channel is sensed busy by the sender although the receiver is free to receive.

139

20 30 40 50 60 70 80 90 1000

2

4

6

8

10

12

14Average Packet Delay vs. Time (@nodes=30)

Time [s]

Ave

rage

Pac

ket D

elay

[s]

CSMACSMApw802.11ALOHAUW−MACsglUW−MACmlt

Figure 59: 2D Deep Water UW-ASNs.Average packet delay vs. simulation time (30sensors,6 uw-gateways)

0 10 20 30 40 50 60 70 80 90 1000

0.5

1

1.5

2

2.5

3

3.5x 10

−4 Average Energy per Bit vs. Time (@nodes=30)

Time [s]

Ave

rage

Use

d E

nerg

y pe

r B

it [J

/bit]


Figure 60: 2D Deep Water UW-ASNs.Average energy per received bit vs. simulation time(30 sensors,6 uw-gateways)

140

5 10 15 20 25 30 35 40 45 50 550

2

4

6

8

10

12Average Packet Delay (LP=250Byte)

Number of Sensors

Ave

rage

Pac

ket D

elay

[s]


Figure 61: 2D Deep Water UW-ASNs.Average packet delay vs. number of sensors

5 10 15 20 25 30 35 40 45 50 550

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2x 10

−4 Average Normalized Used Energy (LP=250Byte)

Number of Sensors

Nor

mal

ized

Use

d E

nerg

y [J

/bit]


Figure 62: 2D Deep Water UW-ASNs.Average normalized used energy vs. number ofsensors

141

5 10 15 20 25 30 35 40 45 50 550.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1Normalized Succesfully Received Packets (LP=250Byte)

Number of Sensors

Nor

mal

ized

Suc

cesf

ully

Rec

eive

d P

acke

ts


Figure 63: 2D Deep Water UW-ASNs.Normalized successfully received packets vs. num-ber of sensors

5 10 15 20 25 30 35 40 45 50 55−20

0

20

40

60

80

100

120

140

160

180No. of Data Packet Collisions (LP=250Byte)

Number of Sensors

No.

of C

ollis

ions


Figure 64: 2D Deep Water UW-ASNs.Number of data packet collisions vs. number ofsensors

142

20 30 40 50 60 70 80 90 1000

1

2

3

4

5


Time [s]

Ave

rage

Pac

ket D

elay

[s]


Figure 65: 3D Shallow Water UW-ASNs.Average packet delay vs. simulation time (30sensors)

the 2D deep water scenario, in this shallow water scenario the overall performance of our

solution is even better with respect to the competing MAC schemes mainly because of the

higher channel reuse achieved. When the number of sensors increases, the implemented

routing algorithm [66] has a higher flexibility in the choice of data paths, which rely more

on multi-hop communications, thus increasing their average number of hops. While at the

routing layer this decreases the expected end-to-end energy to forward packets, higher in-

terference is generated at the MAC layer. Interestingly, both versions of our UW-MAC

solution show very good robustness to this effect, while their competing MAC schemes are

negatively affected, as shown throughout the reported figures (Figs. 65-70). This phenom-

enon is particularly evident in Fig. 69, where the normalized received packet metric drops

below0.45 in all the random-access MAC schemes when50 sensors are deployed, while

UW-MACsgl, and even more UW-MACmlt, have very high performance (UW-MACsgl

over0.80 and UW-MACmlt close to0.95 with 50 sensors).

143

20 30 40 50 60 70 80 90 1000

1

2

3

x 10−4 Average Energy per Bit vs. Time (@nodes=30)

Time [s]

Ave

rage

Use

d E

nerg

y pe

r B

it [J

/bit]


Figure 66: 3D Shallow Water UW-ASNs.Average energy per received bit vs. simulationtime (30 sensors)

5 10 15 20 25 30 35 40 45 50 550

2

4

6

8

10

12


Number of Sensors

Ave

rage

Pac

ket D

elay

[s]


Figure 67: 3D Shallow Water UW-ASNs.Average packet delay vs. number of sensors

144

5 10 15 20 25 30 35 40 45 50 550

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8x 10


Number of Sensors

Nor

mal

ized

Use

d E

nerg

y [J

/bit]


Figure 68: 3D Shallow Water UW-ASNs.Average normalized used energy vs. number ofsensors

5 10 15 20 25 30 35 40 45 50 550.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9


Number of Sensors

Nor

mal

ized

Suc

cesf

ully

Rec

eive

d P

acke

ts


Figure 69: 3D Shallow Water UW-ASNs.Normalized successfully received packets vs.number of sensors

145

5 10 15 20 25 30 35 40 45 50 55−50

0

50

100

150

200

250

300

350

400


Number of Sensors

No.

of C

ollis

ions


Figure 70: 3D Shallow Water UW-ASNs.Number of data packet collisions vs. number ofsensors

6.5.3 Three-dimensional UW-ASNs with Mobile AUVs

We considered a variable number of sensors (from5 to 50) randomly deployed in the 3D

shallow water with volume of500x500x50 m3, and3 AUVs moving in the entire volume

according to the Random Waypoint mobility model. We set the velocity to3 m/s and no

pause between consecutive movements to simulate a worst-case mobility scenario. In all

MAC schemes, AUVs broadcast location update messages when their position has changed

by more than20 m. Figures 71-76 report the overall performance in this simulation setting,

and show the robustness of our MAC solutions against inaccurate node position and inter-

ference information mainly caused by mobility, traffic unpredictability, and packet loss due

to channel impairment. In particular, Figs. 74 and 75 show the dramatic improvements of

UW-MAC over other MAC solutions, both in terms of energy (15µ J/bit vs. 30−40µ J/bit

and over) and normalized received packets (0.7− 0.9 vs. 0.3 for more than35 sensors).

146

20 30 40 50 60 70 80 90 1000

1

2

3

4

5

6


Time [s]

Ave

rage

Pac

ket D

elay

[s]


Figure 71: 3D UW-ASNs with mobile AUVs.Average packet delay vs. simulation time (30sensors)

0 10 20 30 40 50 60 70 80 90 1000

0.5

1

1.5

2

2.5

3

3.5

4

4.5x 10

−4 Average Energy per Bit vs. Time (@nodes=30)

Time [s]

Ave

rage

Use

d E

nerg

y pe

r B

it [J

/bit]


Figure 72: 3D UW-ASNs with mobile AUVs.Average energy per received bit vs. simula-tion time (30 sensors)

147

5 10 15 20 25 30 35 40 45 50 550

2

4

6

8

10


Number of Sensors

Ave

rage

Pac

ket D

elay

[s]


Figure 73: 3D UW-ASNs with mobile AUVs.Average packet delay vs. number of sensors

5 10 15 20 25 30 35 40 45 50 550

0.5

1

1.5

2

2.5

3

3.5x 10


Number of Sensors

Nor

mal

ized

Use

d E

nerg

y [J

/bit]


Figure 74: 3D UW-ASNs with mobile AUVs.Average normalized used energy vs. numberof sensors

148

5 10 15 20 25 30 35 40 45 50 550.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9


Number of Sensors

Nor

mal

ized

Suc

cesf

ully

Rec

eive

d P

acke

ts


Figure 75: 3D UW-ASNs with mobile AUVs.Normalized successfully received packets vs.number of sensors

5 10 15 20 25 30 35 40 45 50 55−50

0

50

100

150

200

250


Number of Sensors

No.

of C

ollis

ions


Figure 76: 3D UW-ASNs with mobile AUVs.Number of data packet collisions vs. numberof sensors

149

CHAPTER VII

CROSS-LAYER COMMUNICATION FOR MULTIMEDIA

APPLICATIONS IN UNDERWATER ACOUSTIC SENSOR

NETWORKS

7.1 Preliminaries

A significant surge in research on underwater sensor networks in the last few years, partly

inspired by our position paper on this topic [7], has resulted in increased interest in the

networking community for this leading-edge technology. Several architectures, protocols,

and solutions for underwater networking have been proposed [67][60][65][66][91].

Moreover, the new recently started ACM International Workshop on UnderWater Net-

works (WUWNet) has been rated in 2006 as the most successful workshop co-located with

the prestigious ACM Conference on Mobile Computing and Networking (MobiCom). This

growing interest can be largely attributed to new applications enabled by underwater net-

works of small devices capable of harvesting information from the physical environment,

performing simple processing on the extracted data and transmitting it to remote locations.

As of today, existing studies on underwater networks are mostly focused on enabling the

measurement of scalar physical phenomena like temperature, water content, or presence

of contaminants in water. In general, most of the applications have very low bandwidth

demands, and are usually delay tolerant.

Another recent trend in the terrestrial sensor networks domain, driven by the avail-

ability of inexpensive hardware such as CMOS cameras and microphones that are able to

ubiquitously capture multimedia content from the environment, is to integrate multimedia

communications in the sensor network paradigm, thus giving rise to the so-called Wireless

150

Multimedia Sensor Networks (WMSNs) [6]. These are networks of wirelessly intercon-

nected devices that allow retrieving video and audio streams, still images, and scalar sensor

data.

Underwater multimedia sensor networks will not only enhance existing sensor network

applications, such as tracking and environmental monitoring, but they will also enable sev-

eral new applications such as: underwater multimedia surveillance, advanced coastal sur-

veillance, environmental monitoring, undersea explorations, disaster prevention, assisted

navigation.

Many of the above applications require the sensor network paradigm to be re-thought

in view of the need for mechanisms to deliver multimedia content with a certain level of

quality of service (QoS). Since the need to minimize the energy consumption and to effi-

ciently utilize the channel has driven most of the research in underwater sensor networks

so far, mechanisms to efficiently deliver application-level QoS, and to map these require-

ments to network-layer metrics such as latency and packet error rate, have not been primary

concerns in mainstream research on underwater sensor networks. Conversely, algorithms,

protocols, and techniques to deliver multimedia content over large-scale networks have

been the focus of intensive research in the last twenty years, especially in ATM wired

and wireless networks. Later, many of the results derived for ATM networks have been

re-adapted, and architectures such as Diffserv and Intserv for Internet QoS delivery have

been developed. However, there are several main peculiarities of sensor networks and of

the underwater environment in particular that make QoS delivery of multimedia content

an even more challenging, and largely unexplored, task such as: characteristics of the un-

derwater channel, resource constraints, variable channel capacity, cross-layer coupling of

functionalities.

We envision that underwater sensor networks will need to provide support and differ-

entiated service for several different classes of applications. In particular, they will need to

provide differentiated service between delay-sensitive and delay-tolerant applications, and

151

loss-sensitive and loss-tolerant applications. Therefore, the main traffic classes that need to

be supported are:

• Delay-tolerant Applications:

– Loss-tolerant, Multimedia Streams.This class includes multimedia streams

that, being intended for storage or subsequent offline processing, do not need to

be delivered within strict delay bounds.

– Loss-tolerant, Data.This may include environmental data from scalar sensor

networks, or non time-critical snapshot multimedia content, with low or mod-

erate bandwidth demand.

– Loss-sensitive, Data.This may include data from critical monitoring processes,

with low or moderate bandwidth demand, that require some form of offline post

processing.

• Delay-sensitive Applications:

– Loss-tolerant, Multimedia Streams.This class includes video and audio streams,

or multi-level streams composed of video/audio and other scalar data (e.g., tem-

perature readings), as well as metadata associated with the stream, that need

to reach a human or automated operator in real-time, i.e., within strict delay

bounds, and that are however relatively loss tolerant (e.g., video streams can be

within a certain level of distortion). Traffic in this class usually has relatively

high bandwidth demand.

– Loss-tolerant, Data.This class may include monitoring data from densely de-

ployed scalar sensors such as light sensors whose monitored phenomenon is

characterized by spatial correlation, or loss-tolerant snapshot multimedia data

(e.g., images of a phenomenon taken from several multiple viewpoints at the

same time). Hence, sensor data has to be received timely but the application is

152

moderately loss-tolerant. The bandwidth demand is usually between low and

moderate.

– Loss-sensitive, Data.This may include data from time-critical monitoring processes

such as distributed control applications. The bandwidth demand varies between

low and moderate.

This chapter is organized as follows. Section 7.2 proposes a general methodology to

design a cross-layer protocol suite to enable efficient communications in a sensor network.

Section 7.3 specializes this framework for underwater multi-hop sensor networks and pro-

poses a cross-layer solution for delay-tolerant UW-ASN applications, while Section 7.4

deals with challenges for delay-sensitive applications. Section 7.5 discusses the protocol

operation and proposes possible mechanisms to improve its efficiency in the case of mobile

AUVs.

7.2 Cross-layer Resource Allocation Framework

As discussed in the previous section, several protocols have been developed for underwater

acoustic communication at different layers of the protocol stack. However, existing pro-

tocols do not consider cross-layer interactions, which play a crucial role in the design of

wireless networks.

The attention of researchers in recent years focused on developing protocols for each

individual layer. We have developed MAC [67] and routing protocols [65][66] considering

the effect of the underwater channel on these protocols. Based on the experience that we

gained in this research domain over the last 4 years, we came to the conclusion that a cross-

layer design approach would be the most suited solution for underwater sensor networks.

Several approaches to cross-layer design are possible.

• Pairwise interactions[19][49]. Resource allocation problems are treated by consid-

ering simple interactions between two communication layers. A typical example is

153

the interaction between the congestion control and power control mechanisms [19].

In [49], the joint power control and scheduling problem is addressed. However, this

approach does not take into account the tight coupling among functionalities handled

at all layers of the protocol stack typical of multi-hop underwater networks.

• Heuristic approaches[53]. Resource allocation problems following this approach

consider interactions between several communication functionalities at different lay-

ers. However, since it is not easy to model and control the interactions between

functionalities, solutions in these category rely on heuristic approaches, thus leading

to suboptimal performance.

• Resource allocation frameworks[72][55]. These approaches integrate different com-

munication functionalities into a coherent mathematical framework and provide a

unified foundation for the cross-layer design and control in multi-hop wireless net-

works. Usually, solutions developed try to reach optimality based on an objective

function that is application dependent and provide guidelines and tools to develop

mathematically sound distributed solutions.

Recent studies [55] have demonstrated the need to integrate various protocol layers into

a coherent framework, to help provide a unified foundation for the analysis of disparate

problems and algorithms in wireless networking. Lately, there has been an increasing in-

terest in research activities that build on recently developed nonlinear (often convex, and

sometimes nonconvex) optimization theory to deal with the design of wireless communi-

cation systems [55]. Our objective is to develop a framework that accurately models every

aspect of the layered network architecture, resulting in theoretical and practical impacts

beyond the previously established results. Our previous experience in modeling function-

alities of the communication stack of underwater networks led us to develop an adaptive

coherent framework that can adapt to different application requirements and seek optimal-

ity in any possible situation. Still, we will seek to develop low-complexity distributed

154

solutions that can be implemented on low-end sensors.

7.2.1 Related Work

Resource allocation in multi-hop ad hoc wireless networks has been extensively studied in

the last years, typically with the objectives of maximizing the network lifetime [18], min-

imizing the energy consumption [58], and maximizing the network capacity [72]. Several

papers in the literature focus on the joint power control and MAC problem, and/or power

control and routing issues, although most of them studies the interactions among different

layers under restricted assumptions, forming a literature that is too large to be exhaustively

reviewed here. Rather, we report a set of significative examples. In [25], the problem of

scheduling maximum number of links in the same time slot is studied. The objective of the

paper is to develop a power control based multiple access algorithm for contention-based

wireless ad hoc networks, so that the network maximum per-hop throughput is achieved.

To this end, the transmit powers are set to their minimum required levels such that all

transmissions achieve a target signal-to-interference-plus-noise ratio (SINR). In [21], the

problem of joint routing, link scheduling, and power control to support high data rates for

broadband wireless multi-hop networks is analyzed. In particular, the work focuses on the

minimization of the total average transmission power in the wireless multi-hop network,

subject to given constraints regarding the minimum average data rate per link, as well as

peak transmission power constraints per node. In [49], the joint power control and schedul-

ing problem is addressed under the assumption that the session paths are already given.

The main contribution in [49] is the formulation of a QoS framework that is able to capture

both the different definitions of QoS from network layer to physical layer and the general

requirements of the individual sessions. By exploiting this framework, [49] showed the

need of close interactions between these layers.

155

7.2.2 Precautionary Guidelines in Cross-layer Design

In this section, we describe possible risks raising when a cross-layer approach is followed,

and propose precautionary guidelines and principles for cross-layer design.

The increased interactions and dependencies across layers turn into an interesting opti-

mization opportunity that may be worth exploiting. Following this intuition, many cross-

layer design papers that explore a much richer interaction between parameters across layers

have been proposed in the recent past. While, however, as an immediate outcome most of

these cross-layer suggestions may yield a performance improvement in terms of through-

put or delay, this result is often obtained by decreasing thearchitecture modularity, and by

loosing the logical separation between designers and developers. This abstraction decou-

pling is needed to allow the former to understand the overall system, and the latter to realize

a more efficient production. For these reasons, when a cross-layer solution is proposed, the

system performance gain needs to be weighed against the possible longer-term downfalls

raised by a diminished degree of modularity.

In [47], the authors reexamine holistically the issue of cross-layer design and its ar-

chitectural ramifications. They contend that a good architectural design leads toprolifera-

tion and longevityof a technology, and illustrate this with some historical examples, e.g.,

John von Neumann’s architecture for computer systems, at the origin of the separation of

software and hardware; the layered OSI architecture for networking, base of the current

Internet architecture success; Shannon’s architecture for communication systems, motivat-

ing the nonobvious separation of source and channel coding; last but not least, the plant

controller feedback paradigm in control systems, providing universal principles common

to human engineered systems as well as biological systems.

Although the concerns and cautionary advice expressed in [47] about cross-layer design

are sound and well motivated, the layered-architecture, which turned to be a successful de-

sign choice for wired networks, may need to be carefully rethought for energy-constrained

WSNs, where the concept itself of ‘link’ is labile, and many different effective transmission

156

schemes and communication paradigms are conceivable.

This is also the conclusion drawn in [89], where the pros and cons of cross-layer design

approach are evaluated. In [89], cross-layer design to improve reliability and optimize

performance is advocated, although the design needs to be cautiously developed to provide

long-term survivability of cross-layer architectures. In the following, we present some

concerns and precautionary considerations, which need to be considered when a cross-

layer design architecture is proposed, and suggest some possible research directions.

• Modularity. In the classical layered design approach, a system architecture is bro-

ken down intomodular components, and theinteractionsanddependenciesbetween

these components are systematically specified. This design philosophy allows to

break complex problems into easier subproblems, which can then be solved inisola-

tion, without considering all the details pertaining the overall system. This approach

guarantees the inter-operability of subsystems in the overall system once each sub-

system is tested and standardized, leading to quick proliferation of technology and

mass production. Conversely, a cross-layer design approach may loose the decou-

pling between design and development process, which may impair both the design

and the implementation development and slow the innovation down.

• System enhancement.Design improvements and innovations may become difficult

in a cross-layer design, since it will be hard to assess how a new modification will

interact with the already existing solutions. Furthermore, a cross-layer architecture

would be hard to upkeep, and the maintaining costs would be high. In the worst cases,

rather than modifying just one subsystem, the entire system may need to be replaced.

For these reasons, we advocate keeping some degree of modularity in the design

of cross-layer solutions. This could be achieved by relying on functional entities -

as opposed to layers in the classical design philosophy - that implement particular

functions. This would also have the positive consequence of limiting the duplication

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of functions that often characterizes a layered design. This functional redundancy is,

in fact, one the cause for poor system performance.

• Instability. In cross-layer design, the effect of any single design choice may affect the

whole system, leading to various negative consequences such as instability. This is a

non trivial problem to solve, since it is well known from control theory that stability

is a paramount issue. Moreover, the fact that some interactions are not easily foreseen

makes cross-layer design choices even trickier. Hence, great care should be paid to

prevent design choices from negatively affecting the overall system performance. To

this purpose, there is a need to integrate and further develop control theory techniques

to study stability properties of system designed following a cross-layer approach.

Dependency graphs, which may be used to capture the dependency relation between

parameters, could be valuable means to prove stability, although hard to implement

in some cases.

• Robustness.Besides stability, there is also the issue of robustness. Robustness is the

property of a system to be able to absorb parameter uncertainties and, in general, the

degrading effect on the overall performance experienced by a system when unpre-

dictable events occur such as transmission anomalies, channel impairments, loss of

connectivity, etc. Techniques such astimescale separationandperformance tracking

and verificationmay need to be employed to separate interactions and verify on-the-

fly the system performance. Moreover, an accompanying theoretical framework may

be needed to fully support cross-layer design and study its robustness properties.

7.2.3 General Framework for Cross-layer Optimization

As previously discussed, our objective is to formulate cross-layer resource allocation prob-

lems in multi-hop wireless underwater networks as (possibly convex) optimization prob-

lems. While in this section we outline a general framework where different resource allo-

cation problems will fit by specifying the form of particular functions, in Section 7.3 we

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specialize the framework for the underwater environment.

The general framework for cross-layer optimization problem can be cast as follows:

POpt: Cross-Layer Problem for Optimal Resource Allocation

Given : P es , dij(), lij(), Bs (69)

Find : r , F, p (70)

Maximize :∑

s∈S Us(rs) + sumj∈NVj(pj) (71)

Subject to :

∑s∈S

f sij · rs ≤ lij(Pe, p); (72)

∑

(i,j)∈Ef s

ij · dij(r , lij(Pe, p)) ≤ Bs; (73)

F ∈ Ffeas(r), r ∈ Rfeas, p ∈ Pfeas. (74)

The following notations are introduced in the above problem:

• r = [r1, r2, .., rs, .., r|S|] is the vector whose generic elementrs represents the bit rate as-

signed to sources ∈ S; p = [p1, p2, .., pj , .., p|N |] is the transmission power vector, where the

generic elementpj is the transmission power assigned to nodej ∈ N ; F = [fsij ] is a binary

matrix that represents the routing decisions, where the generic elementfsij equals 1 iff link

(i, j) is part of the end-to-end path associated with sources; Pe = [P e1 , P e

2 , .., P ej , .., P e

|N |] is

a vector whose generic elementP ej represents the decoding error probability desired by node

j ∈ N ;

• dij() is the delay expression associated to link(i, j), that models the specific physical and

MAC layer, and their interaction with the routing and congestion control functions;lij() is

the capacity expression associated to link(i, j), that depends on the physical layer character-

istics;Bs is the delay bound associated to sources;

• Us andVj are utility functions in the objective function, which model the desired optimality

characteristics of the network, according to the application requirements.

159

The above formulation jointly models problems at different layers in a cross-layer fash-

ion. The optimization variables, whose values have to be jointly determined, are associated

to different resources at different layers of the protocol stack. Thetransport problemcon-

sists of deciding the bit rate vectorr to be assigned to the set of sources in the network.

The routing problemconsists of determining the routing matrixF according to which the

sources route their data flows. Thephysical problemconsists of selecting the optimal trans-

mission power vectorp that the set of sources should use. The above variables have to be

jointly selected in order to maximize the objective function in (71). In particular, (71) max-

imizes the sum of the utilities of each sources ∈ S and of each nodej ∈ N , according

to the utility functionsUs andVj, respectively. While the former increases with increasing

bit rates granted at each sources, the latter increases with decreasing power assigned to

each node. Note that the heterogeneous characteristics of underwater sources and nodes in

the network can be captured by the simultaneous use of different utility functions. Con-

straint (72) imposes that the resource utilized on each link be lower than the link capacity,

which depends on the desired decoding error probability vector and on the used transmis-

sion powers. Constraint (73) forces the end-to-end delay of each source to be bounded by

the maximum tolerated delay. The delay on each link can be expressed as a function of

the assigned vector rate and link capacity. The constraints in (74) impose limitations on

the routing decisions, the available bit rates, and the selectable transmission powers, re-

spectively, considering the MAC and physical constraints. Specifically, in the underwater

routing decision an end-to-end path is considered feasible if it is composed only of links

connecting adjacent nodes. Moreover, concurrent transmissions are considered feasible if

the generated interference is within certain bounds.

In the next section, we explain how we intend to specialize the above framework for

underwater multi-hop acoustic networks. In particular, we do so by:

• Identifying adequate utility functions.We identify utility functions that: i) represent

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the desired global design objectives; ii) exhibit properties, e.g., convexity, that al-

low finding a unique global optimum with efficient methods such as the primal-dual

interior point algorithm [12].

• Specifying details of the physical layer.We integrate in the framework underwa-

ter physical layer characteristics, such as error control and the characteristics of the

underwater channel, that mainly impact the overall resource allocation problem.

7.3 Cross-layer Routing/MAC/PHY Solution for Delay-tolerant Appli-cations

The proposed algorithm is a distributed routing/MAC/PHY solution for different traffic

classes, and allow each node tojointly select its best next hop, the optimal transmitted

power, the code length, and the forward error correction (FEC) rate for each packet, with

the objective of minimizing the energy consumption, while taking the condition of the

underwater physical channel and the application requirements into account. The proposed

solution relies on a geographical routing paradigm. Geographical routing protocols are

very promising for their scalability feature and limited required signaling.

In the following, we assume that the channel is heavily affected by multipath (saturated

condition, see [70]) as it is often the case in shallow water [7]. In this environment, the

signal fading can be modeled by a Rayleigh r.v., which accounts for a worst-case scenario,

and the transmission loss betweeni andj is TLij · ρ2, whereTLij = dij · 10[α(f)·dij+A]/10,

with A ∈ [5, 10] dB, andρ has a unit-mean Rayleigh cumulative distributionDρ(ρ) =

1−exp(−πρ2/4). Let us define thesignal transmission marginfor link (i, j) asmij, where

P ∗ij ·m2

ij [W] is the actual transmit power, whileP ∗ij [W] represents the optimal transmission

power in an ideal channel, i.e., the transmit power before applying the margin to face the

fading dips. The packet error ratePERij experienced on link(i, j) when senderi transmits

powerP ∗ij ·m2

ij can be defined as the probability that the received power at nodej be smaller

161

than that required in an ideal channel where no multipath is experienced, i.e.,

PERij = Pr{

P ∗ij ·m2ij

TLij ·ρ2 ≤ Pij∗

TLij

}= Pr

{ρ ≥ mij

}= 1−Dρ(mij) = exp

(− πm2

ij

4

).

(75)

Hence, the average number of transmissions of a packet such that receiverj correctly de-

codes it when it is sent with signal transmission marginmij isNTij (mij) = [1−PERij]

−1 =

Dρ(mij)−1. This relation assumes independent errors among adjacent packets, which holds

when the channel coherence time is shorter than the retransmission timeout, i.e., the time

before retransmitting an unacknowledged packet. Note that, while for loss-sensitive appli-

cations a packet is locally retransmitted until it is correctly decoded at the receiver (and

the average number of transmissions isNTij (mij)), for loss-tolerant applications packets

may be protected unequally, depending on the importance of the data they are carrying for

correct perceptual reconstruction.

The main features that characterize our cross-layer solution are: i) it provides aunique

and flexible solutionfor different architecture such asstatic2D deep water and 3D shallow

water, and scenarios withmobileAUVs; ii) it is fully distributed, since spreading codes,

transmit power, and next hop are distributively selected by each sender without relying on

a centralized entity; iii) it isintrinsically secure, since it uses chaotic codes; iv) itfairly

sharesavailable resources among active devices; v) itefficiently supports multicast trans-

missions, since spreading codes are decided at the transmitter side; and vi) it isrobust

against inaccurate node position and interference information caused by mobility, traffic

unpredictability, currents, and control packet loss caused by channel impairment.

According to our distributed routing/MAC/PHY algorithm, nodei will select j∗ as its

best next hop iff

j∗ = argminj∈Si∩PNi

E(j)i

∗, (76)

whereE(j)i

∗represents the minimum energy required to successfully transmit a payload bit

from nodei to the sink, taking the condition of the underwater channel and the interference

state atj into account, wheni selectsj as next hop. Moreover, in (76),Si is theneighbor

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setof nodei, while PNi is thepositive advance set, composed of nodes closer to sinkN

than nodei, i.e.,j ∈ PNi iff djN < diN

This link metric, objective function (77) inPcrosslayer, takes into account the number of

packet transmissions (NTij ) associated with link(i, j), given the optimal packet size (L∗P ),

and the optimal combination of FEC(LFP ij

∗) and transmitted power(P ∗

ij). Moreover, it

accounts for the average hop-path length(NHopij ) from nodei to the sink whenj is selected

as next hop, by assuming that the following hops will guarantee the same advance towards

the surface station (sink). While this approach to estimate the number of remaining hops

towards the surface station is simple, several advantages can be pointed out, as described

in [88], such as: i) it does not incur any signaling overhead since it is locally computed and

does not require end-to-end information exchange; ii) its accuracy increases as the density

increases; iii) its accuracy increases as the distance between the surface station and the

current node decreases. For these reasons, we decided to use this method rather than trying

to estimate the exact number of hops towards the destination.

We can now cast the cross-layer Routing/MAC/PHY optimization problem in a Rayleigh

channel.

Pcrosslayer: Cross-layer Routing/MAC/PHY Optimization Problem

Given : i, Si, PNi , L∗P , LH

P , Ebelec, r, N0j, Pmax

i , TLij, NIj, BERj, Sk, NIk,∀k ∈ Ki

Find : j∗ ∈ Si ∩ PNi , P ∗

ij∗ ≤ Pmaxi , c∗ij ∈ [cmin, cmax], m∗

ij ∈ R+



P −LFP ij

· NTij (mij) · NHop

ij (77)

Subject to :

Ebij = 2 · Eb

elec +Pij ·m2

ij

r/cij

; (78)

LFP ij = ΨF−1

(L∗P , PERij, Φ

M(

Pij

N0j · (r/cij) · TLij

)); (79)

NHopij = max

(diN

< dij >iN

, 1

); (80)

163

NTij (mij) = Dρ(mij)

−1 =

[1− exp

(− πm2

ij

4

)]−1

; (81)

Pminij (cij) ≤ Pij ≤ min [Pmax

ij , Pmaxi ]; (82)

Pminij (cij) ≤ Pij ·m2

ij ≤ min [Pmaxij , Pmax

i ]; (83)

where

Pminij (cij) =

NIj · TLij

α · cij · Φ(BERj)=

Γij

cij

, (84)

Γij =NIj · TLij

α · Φ(BERj), (85)

Pmaxij = min

k∈Ki


]. (86)

We introduce the following notations that are used in the cross-layer protocol suite

optimization problem:

• L∗P = LHP + LF

P ij + LNP ij [bit] is thefixed optimal packet size, solution of anoff-

line optimization problem presented in [66], whereLHP is thefixedheader size of a

packet, whileLFP ij is thevariable FEC redundancy that is included in each packet

transmitted from nodei to j; thus,LNP ij = L∗P − LH

P − LFP ij is thevariablepayload

size of each packet transmitted in a train on link(i, j).

• Ebelec = Etrans

elec = Erecelec [J/bit] is thedistance-independentenergy to transit one bit,

whereEtranselec is the energy per bit needed by transmitter electronics (PLLs, VCOs,

bias currents, etc.) and digital processing, andErecelec represents the energy per bit

utilized by receiver electronics. Note thatEtranselec does not represent the overall energy

to transmit a bit, but only the distance-independent portion of it.

• Ebij = 2 ·Eb

elec+Pij ·m2

ij

r/cij[J/bit] accounts for the energy to transmit one bit from nodei

to nodej, when the transmitted power and the bit rate arePij [W] andr/cij [bps], re-

spectively. The second term represents thedistance-dependentportion of the energy

necessary to transmit a bit.

• Pmaxi [W] is the maximum transmitting power for nodei.

164

• ΦM(

P ij

N0j(r/cij)·TLij

)represents the average bit error rate on link(i, j); it is a function

of the ratio between the average energy of the received bitP ij/((r/cij) · TLij) and

the expected noiseN0j at the receiver, and it depends on the modulation schemeM;

in general, the noise has a thermal, an ambient, and a man-made component; studies

of shallow water noise measurements [34] suggest considering an average value of

70 dBµPa for the ambient noise.

• ψF(LP , LF

P , BER)

represents the average packet error rate(PER), given the packet

sizeLP , the FEC redundancyLFP , and the average bit error rate(BER), and it de-

pends on the adopted FEC techniqueF .

• PERe2emax is the application maximum allowed end-to-end packet error rate, while

NHopmax is the maximum expected number of hops, function of the network diameter

[74].

• TLij [dB] is the transmission loss fromi to j, which is computed according to the

Urick model [90].

• NTij is the average number of transmissions of a packet sent by nodei such that the

packet is correctly decoded at receiverj.

• NHopij = max

(diN

<dij>iN, 1

)is the estimated number of hops from nodei to the surface

station (sink)N whenj is selected as next hop, wheredij is the distance betweeni

andj, and< dij >iN (which we refer to asadvance) is the projection ofdij onto the

line connecting nodei with the sink.

• BERij = φM(Ebrec/N0j) represents the bit error rate on link(i, j); it is a function

of the ratio between the energy of the received bit,Ebrec = P TX

ij /(r · TLij), and the

expected noise at nodej, N0j, and it depends on the adopted modulation schemeM.

• LFP ij = ψF−1

(L∗P , PERij, BERij) returns the needed FEC redundancy, given the

165

optimal packet sizeL∗P , the packet error rate and bit error rate on link(i, j), and it

depends on the adopted FEC techniqueF .

Each nodei needs to i) limit the near-far effect when it transmits toj and ii) avoid

impairing ongoing communications, which is expressed by the following

N0+IjPij

TLij

≤ wij · Φ(BERj)

N0+Ik+Pij

TLik

Sk≤ wtkk · Φ(BERk), ∀k ∈ Ki.

(87)

In (87), N0 [W] is the average noise power,Ij and Ik [W] are the MAI at nodesj and

k ∈ Ki, withKi being the set of nodes whose ongoing communications may be affected by

nodei’s transmit power. In addition,wij andwtkk are the bandwidth spreading factors of

the ongoing transmissions fromi to j and fromtk to k, respectively, wheretk is the node

from whichk is receiving data. Furthermore,Pij [W] represents the power transmitted by

i to j when an ideal channel (without multipath, i.e.,A = 0 dB) is assumed, i.e., when

no power margin is considered to face the fading dips. Finally,TLij andTLik are the

transmission losses fromi to j and fromi to k ∈ Ki, respectively, whileSk [W] is the user

signal power that receiverk is decoding, andΦ() is the MAI threshold, which depends on

the target bit error rate(BER) at the receiver node (see [61]). We will denote the noise and

MAI power of a generic noden asNIn = N0 + In, and the normalized received spread

signal, i.e., the signal power after despreading, asSn = Sn · wtnn · Φ(BERn).

The first constraint in (87) states that the SINR−1 at receiverj needs to be below a

certain threshold, i.e., the powerPij transmitted byi needs to be sufficiently high to allow

receiverj to successfully decode it, given its current noise and MAI power level(NIj). The

second constraint in (87) states that the SINR−1 at receiversk ∈ Ki must not be above a

threshold, i.e., the powerPij transmitted byi must not impair the ongoing communications

toward nodesk ∈ Ki. By combining the constraints in (87), we obtain

NIj ·TLij

wij ·Φ(BERj)≤ Pij ≤ mink∈Ki


]. (88)

166

Consequently, to set its transmit powerPij and spreading factorwij, nodei needs to lever-

age information on the MAI and normalized receiving spread signal of neighboring nodes.

This information is broadcast periodically by active nodes. In particular, to limit such

broadcasts, a generic noden transmits only significant values ofNIn and Sn, i.e., out

of predefined tolerance ranges. Constraints (88) are incorporated in the cross-layer rout-

ing/MAC/PHY optimization problemPcrosslayer in (82) and (83).

The choice of a fixed packet size for UW-ASNs is motivated by the need for system

simplicity and ease of sensor buffer management. In fact, a design proposing per-hop opti-

mal packet size, e.g., solving an optimization problem for any link distance and using the

resultingdistance-dependentoptimal packet size in the routing algorithm, would encounter

several implementation problems, such as the need for segmentation and re-assembly func-

tionalities that incur tremendous overhead, which are unlikely affordable by low-end sen-

sors. In [66], the packet size is optimized given the distance distribution between neighbor-

ing nodes, which determines the average transmission loss, and ultimately the bit error rate,

computed as a functionΦM() of the modulation schemeM and the average signal-to-noise

ratio at the receiver.

The link metricE(j)i

∗in (76) stands for the optimal energy per payload bit wheni trans-

mits a packet train toj using the optimal combination of powerP ∗ij and FEC redundancy

LFP ij

∗ to achieve link reliability, jointly found by solving problemPcrosslayer. This interpreta-

tion allows nodei to optimally decouplePcrosslayer into two sub-problems: first, minimize the

link metric E(j)i for each of its feasible next-hop neighbors; second, pick as best next hop

that nodej∗ associated with the minimal link metric. This means that the generic nodei

does not have to solve a complicated optimization problem to find its best route towards a

sink. Rather, it only needs to sequentially solve the two aforementioned low-complexity

subproblems, each characterized by a complexityO(|Si ∩ PNi )|, i.e., proportional to the

number of its neighboring nodes with positive advance towards the sink. Moreover, this

operation does not need to be performed each time a sensor has to route a packet, but only

167

when the channel conditions have consistently changed. To summarize, the proposed rout-

ing/MAC solution allows nodei to select as next hop that nodej∗ among its neighbors that

satisfies the following requirements: i) it is closer to the surface station thani, and ii) it

minimizes the link metricE(j)i

∗.

7.4 Cross-layer Routing/MAC/PHY Solution for Delay-sensitive Appli-cations

Similarly to the cross-layer protocol solution tailored for delay-tolerant applications, a com-

munication protocol for delay-sensitive applications should allow each node to distribu-

tively select the optimal next hop, transmitting power, code length, and FEC packet rate,

with the objective of minimizing the energy consumption. However, a protocol solution

tailored for delay-sensitive applications should also include new constraints to statistically

meet the delay-sensitive requirements, such as:

1. The end-to-end packet error rate should be lower than an application-dependent

thresholdPERe2emax, which is reflected in the following:

1−(

1− PERij

)dNHopij +N

(m)HC e

≤ PERe2emax; (89)

2. The probability that the end-to-end packet delay be over a delay boundBmax, should

be lower than an application-dependent parameterγ:

dij

qij

+ δ · σqij ≤ min

m=1,..,M

(∆B

(m)i

NHopij

)− Qij − L∗P

r. (90)

where

• PERe2emax andBmax [s] are the application-dependent end-to-end packet error rate

threshold and delay bound, respectively;

• ∆B(m)i = Bmax−

(t(m)i,now − t

(m)0

)[s] is the time-to-live of packetm arriving at node

i, wheret(m)i,now is the arriving time ofm at i, andt

(m)0 is the timem was generated,

which is time-stamped in the packet header by its source;

168

• N(m)HC is the hop count, which reports the number of hops of packetm from the source

to the current node;

• Tij =L∗Pr

+ T qij [s] accounts for the packet transmission delay and the propagation

delay associated with link(i, j); we will consider a Gaussian distribution forTij, i.e.,

Tij v N(

L∗Pr

+ T qij, σ

qij

2)

;

• Qi [s] andQj [s] are the average queueing delays of nodei (at the time the node

computes its train next hop), and nodej, which is a neighbor node ofi;

• Qij [s] is the network queueing delay estimated by nodei when j is selected as

next hop, computed according to the information carried by incoming packets and

broadcast by neighboring nodes.

As a design guideline to meet these requirements, differently from the cross-layer proto-

col for delay-tolerant applications, a protocol tailored for delay-sensitive applications may

not retransmit corrupted or lost packets at the link layer. Rather, it should discard corrupted

packets. Moreover, it should time-stamp packets when they are generated by a source so

that it could discard expired packets. To save energy, while statistically limiting the end-

to-end packet delay, anearliest deadline firstscheduling may be successfully developed to

dynamically assign higher priority to packets closer to their deadline.

An open problem in the design of a cross-layer solution for delay-sensitive applications

is how to dynamically adjust the packet error rate that will be experienced by a packet

on a link to respect the application end-to-end packet error rate requirement (PERe2emax),

given the estimated number of hops to reach the sink if a specific node is selected as next

hop. Also, the objective function of the optimization problem should be adjusted since no

selective packet retransmission would be performed.

Other important open problems in the design of a cross-layer solution for efficient com-

munication for delay-sensitive UW-ASN applications are:

169

• Unreliability of the Underwater Channel. Streaming of multimedia data over an

underwater sensor network is particularly challenging because of the QoS require-

ments of a video/audio stream, the bandwidth constraints, and the unreliability of

the underwater medium. For example, for good quality video perception a frame

loss rate lower than10−2 is required. This constitutes a hard task since the underwa-

ter channel is highly unreliable, mostly caused by multipath fading and noise at the

physical layer, and by collisions or co-channel interference at the MAC layer. Au-

tomatic repeat request (ARQ), mechanisms use bandwidth efficiently at the cost of

additional latency. Hence, while carefully designed selective repeat schemes may be

of some interest, naive use of ARQ techniques is clearly infeasible for applications

requiring quasi real-time delivery of multimedia content in underwater networks.

• Unequal Error Protection. An important characteristic of multimedia content is

unequal importance, i.e., not all packets have the same importance for correct per-

ceptual reconstruction of the multimedia content. Moreover, in case ofloss-tolerant

multimedia data, even if some errors are introduced, the original information may

still be reconstructed with tolerable distortion. Therefore, an idea that has been used

effectively consists of applying different degrees of FEC to different parts of the

video stream, depending on their relative importance (unequal protection). For ex-

ample, this idea can be applied to layered coded streams to provide graceful degrada-

tion in the observed image quality in presence of error losses, thus avoiding so-called

“cliff” effects [36].

• Joint Source and Channel Coding.In general, delivering error-resilient multime-

dia content and minimizing energy consumption are contradicting objectives. For

this reason, and because of the time-varying characteristics of the underwater chan-

nel, several joint source and channel coding schemes have been developed, e.g., [24],

170

which try to reduce the energy consumption of the whole process. Some recent pa-

pers [56][96] even try to jointly reduce the energy consumption of the whole process

of multimedia content delivery, i.e., jointly optimize source coding, channel coding,

and transmission power control. In these schemes, the image coding and transmission

strategies are adaptively adjusted to match current channel conditions by exploiting

the peculiar characteristics of multimedia data, such as unequal importance of dif-

ferent frames or layers. However, most of these efforts have originated from the

multimedia or coding communities. Thus, not only these papers do not consider the

unique characteristics of the underwater channel, but they are not even reminiscent

of other important networking aspects of content delivery over a multi-hop wireless

networks of memory-, processing- and battery-constrained devices.

• Multimedia In-network Processing. Processing of multimedia content has mostly

been approached as a problem isolated from the network-design problem, with a few

exceptions such as joint source-channel coding [24] and channel-adaptive streaming

[33]. Hence, research that addressed the content delivery aspects has typically not

considered the characteristics of the source content and has primarily studied cross-

layer interactions among lower layers of the protocol stack. However, the processing

and delivery of multimedia content are not independent and their interaction has a

major impact on the levels of QoS that can be delivered. Hence, the QoS required at

the application level will be delivered by means of a combination of both cross-layer

optimization of the communication process, and in-network processing of raw data

streams that describe the phenomenon of interest from multiple views, with different

media, and on multiple resolutions.

7.5 Protocol Operation of the Cross-layer Solution

Our proposed cross-layer solution aims at setting the optimal combination of next hop,

transmit power, and code length at the transmitter side relying on local periodic broadcasts

171

of MAI values from active nodes. Here, nodei needs to transmit a data packet toj, without

impairing ongoing communications fromh tok and fromt ton. Since the system efficiency

is limited by the amount of total interference, it is crucial fori to optimize its transmission,

in terms of transmit power and code length, to limit the near-far problem.

Once the optimization problem has been solved at senderi, and the optimal next hop,

transmitting power, and code length have been found, nodei randomly access the channel

transmitting a short header calledExtended Header (EH). The EH, of sizeLEH bits, is sent

using acommon chaotic codecEH known by all devices at the maximum rate (minimum

code length). Senderi transmits to its next hopj, locateddij meters apart, the short header

EH. The EH contains information about the final destination, i.e., the surface station, the

chosen next hop, i.e., nodej, and the parameters thati will use to generate thechaotic

spreading codefor the actual data packet, of sizeLD bits, thatj will receive fromi. Imme-

diately after the transmission of the EH,i transmits the data packet on the channel, which

is characterized by a raw chip rater [cps] and expected sound velocityq ≈ 1500 m/s,

using the optimal transmit powerP ∗ij [W] and code lengthc∗ij set by the power and code

self-assignment algorithm. If no collision occurs during the reception of the EH, i.e., ifi

is the only node transmitting an EH in the neighborhood of nodej, j will be able to syn-

chronize to the signal fromi, despread the EH using the common code, and acquire the

carried information. At this point, if the EH is successfully decoded, receiverj will be able

to locally generate the chaotic code thati used to send its data packet, and set its decoder

according to this chaotic code in such a way as to decode the data packet. Oncej has

correctly received the data packet fromi, it acknowledges it by sending an ACK packet, of

sizeLA bits, to j using codecA. In casei does not receive the ACK before a timeoutTout

expires, it will keep transmitting the packet until a maximum transmission numberNTmax

is reached. The timeout must be tuned considering the long propagation and transmission

delays, i.e.,Tout ≥ cEH · LEH/r + cij · LD/r + 2dij/q + cA · LA/r.

Note that if senderi does not have updated information about the MAI inj, it increases

172

the code length every time a timeout expires to improve the probability that the packet is

successfully decoded, i.e.,cNT

ij

ij = min [cNT

ij−1

ij · 2β, cmax], where1 ≤ NTij ≤ NT

max and

β ∈ R+.

To enhance the protocol performance and limit the signaling overhead in the case of mo-

bile AUVs, we will introduce a hybrid location management scheme to handle the mobility

of AUVs with minimal energy expenditure for the sensors. The proposed solution will

overcome the drawbacks of previously proposed localization services [54][22] for wire-

less networks. In general, the objective of these mechanisms, which can be classified as

rendezvous-based protocols [22], is to potentially allow each single device in the network to

retrieve the location of any other node, based on queries and replies. Clearly, query-based

mechanisms can introduce delays that may not be acceptable in delay-sensitive applica-

tions. Moreover, the extensive message exchange and complex server structures, often

hierarchical, associated with these protocols, should be avoided in UW-ASNs.

The idea of our hybrid location management scheme is that AUVs broadcast updates

limiting their scope based on Voronoi diagrams extended for three-dimensional networks,

while underwater sensors predict the movements of AUVs based on Kalman filtering of

previously received updates. This scheme is aimed at reducing the energy consumption on

underwater sensors and the acoustic channel utilization by avoiding location updates. We

will develop a proactive location management approach based on update messages sent by

mobile AUVs to sensors. In the spatial domain, broadcasts can be limited based on 3D

Voronoi diagrams. At the same time, AUV movement may be to some extent predictable.

Hence, in the temporal domain, location updates can be limited toAUV positions that

cannot be predictedat the sensor side. Location updates are triggered at the AUVs when the

actual position of the AUV is “far” from what can be predicted at the sensors based on past

measurements. Therefore, AUVs that move following predictable trajectories will need to

update their position much less frequently than AUVs that follow temporally uncorrelated

trajectories.

173

CHAPTER VIII

CONCLUSION

In this thesis, we explored fundamental key aspects of underwater acoustic communica-

tions, proposed communication architectures for underwater acoustic sensor networks, and

developed efficient sensor communication protocols tailored for the underwater environ-

ment. The ultimate objective of this work is to encourage research efforts to lay down

fundamental bases for the development of new advanced communication techniques for

efficient underwater communication and networking for enhanced ocean monitoring and

exploration applications. The thesis was organized in eight chapters.

In Chapter 1, we briefly described the background of this work, and presented the or-

ganization of the thesis.

In Chapter 2, we presented an overview of the state of the art in underwater acoustic

sensor network. We described the challenges posed by the peculiarities of the underwater

channel with particular reference to monitoring applications for the ocean environment. We

discussed characteristics of the underwater channel and outlined future research directions

for the development of efficient and reliable underwater acoustic sensor networks.

In Chapter 3, deployment strategies for two-dimensional and three-dimensional archi-

tectures for underwater sensor networks were proposed, and deployment analysis was pro-

vided. The objectives were to determine the minimum number of sensors to be deployed

to achieve the application-dependent target sensing and communication coverage; provide

guidelines on how to choose the deployment surface area, given a target region; study the

robustness of the sensor network to node failures, and provide an estimate of the number

of required redundant sensors.

In Chapter 4, the problem of data gathering in a 3D underwater sensor network was

174

investigated, by considering the interactions between the routing functions and the charac-

teristics of the underwater acoustic channel. A model characterizing the acoustic channel

utilization efficiency was developed to investigate fundamental characteristics of the under-

water environment, and to set the optimal packet size for underwater communications given

the application requirements. Two distributed routing algorithms were also introduced, for

delay-insensitive and delay-sensitive applications, respectively, with the objective of min-

imizing the energy consumption taking the varying condition of the underwater channel

and the different application requirements into account. The proposed routing solutions

were shown to achieve the performance targets of the underwater environment by means of

simulation.

In Chapter 5, a resilient routing solution tailored for long-term critical monitoring mis-

sions was proposed. Its effectiveness in providing energy-efficient data paths and its ro-

bustness to sensor failures were evaluated by means of simulation.

In Chapter 6, UW-MAC, a distributed MAC protocol for underwater acoustic sen-

sor networks, was proposed. It is a transmitter-based CDMA scheme that incorporates

a closed-loop distributed algorithm to set the optimal transmit power and code length. It is

proven that UW-MAC manages to simultaneously achieve high network throughput, lim-

ited channel access delay, and low energy consumption in deep water communications,

which are not severely affected by multipath. In shallow water communications, which are

heavily affected by multipath, UW-MAC dynamically finds the optimal trade-off among

these objectives. Experiments showed that UW-MAC outperforms competing MAC proto-

cols under all considered network architecture scenarios and simulation settings.

In Chapter 7, a cross-layer resource allocation problem was formulated in multi-hop

wireless underwater networks as an optimization problem. While we first outlined a gen-

eral framework where different resource allocation problems will fit by specifying the form

of particular functions, then we specialized the framework for the underwater environment.

175

We strongly advocated the use of a cross-layer approach to jointly optimize the main net-

working functionalities in order to design communication suites that are adaptable to the

variability of the characteristics of the underwater channel and optimally exploit the ex-

tremely scarce resources.

176

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LIST OF ACRONYMS

ACCP Acoustic Correlation Current Profilers

ACM Association for Computing Machinery

AODV Ad Hoc On Demand Distance Vector Routing

ARQ Automatic Repeat Request

AUV Autonomous Underwater Vehicle

BER Bit Error Rate

CDMA Code Division Multiple Access

CSMA Carrier Sense Multiple Access

CTS Clear To Send

DFE Decision Feedback Equalizer

DPSK Differential Phase Shift Keying

DSDV Destination Sequenced Distance Vector

DSR Dynamic Source Routing

DSSS Direct Sequence Spread Spectrum

EDF Earliest Deadline First

FAMA Floor Acquisition Multiple Access

FDMA Frequency Division Multiple Access

FEC Forward Error Correction

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FHSS Frequency Hopping Spread Spectrum

FIFO First Input First Output

FSK Frequency Shift Keying

GFG Greedy Face Greedy Routing

GPS Global Positioning System

GPSR Greedy Perimeter Stateless Routing

IEEE Institute of Electrical and Electronics Engineers

ILP Integer Linear Programming

IP Internet Protocol

ISI Inter Symbol Interference

MAC Medium Access Control

MACA Multiple Access with Collision Avoidance

MAI Multiple Access Interference

MANET Mobile Ad Hoc Network

MUD Multi User Detection

OFDM Orthogonal Frequency Division Multiplexing

OLSR Optimized Link State Routing

PER Packet Error Rate

PLL Phase Locked Loop

PSK Phase Shift Keying

PTKF Partial Topology Knowledge Forwarding

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QAM Quadrature Amplitude Modulation

QoS Quality of Service

RF Radio Frequency

RTS Request To Send

RTT Round Trip Time

SACK Selective Acknowledge

SDRA Segmented Data Reliable Transport

SDV Small Delivery Vehicle

SINR Signal-to-Interference-plus-Noise Ratio

SNR Signal-to-Noise Ratio

SUD Single User Detection

TCP Transmission Control Protocol

TDMA Time Division Multiple Access

UUV Unmanned Underwater Vehicle

UW-A UnderWater Acoustic

UW-ASN UnderWater Acoustic Sensor Network

UW-MAC UnderWater Medium Access Control

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VITA

Dario Pompili graduated in Telecommunications Engineering (summa cum laude) from the Uni-

versity of Rome ‘La Sapienza’, Italy, in May2001. Since June2001, he had been working at

the same university on the EU IST BRAHMS and SATIP6 projects as a Ph.D. student. In Spring

2003, he worked on sensor networks at the Broadband and Wireless Networking Laboratory, Geor-

gia Institute of Technology, Atlanta, as a Visiting Researcher. In November2004, he earned from

the University of Rome ‘La Sapienza’ the Ph.D. Degree in System Engineering. Since2004, he

is pursuing the Ph.D. Degree in Electrical and Computer Engineering at the Georgia Institute of

Technology under the guidance of Dr. I. F. Akyildiz. In2005, he was awarded Georgia Institute

of Technology BWN-Lab Researcher of the Year for“outstanding contributions and professional

achievements”. Starting August2007, he is an Assistant Professor in the Electrical and Computer

Engineering Department at Rutgers, the State University of New Jersey. His main research inter-

ests are in wireless ad hoc and sensor networks, underwater acoustic sensor networks, and overlay

networks. Over the last few years he has authored and co-authored10 journal and25 conference

refereed publications, as well as2 book chapters and6 submitted papers. He is a member of the

IEEE Communications Society and the ACM Communication Society.

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efficient communication protocols for underwater

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